{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from FL 3-10-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)         \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ########################\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections only count if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     \"\"\"\n",
    "    #global borderUnits\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        trySet = set(list())\n",
    "        for U in addedList:\n",
    "            trySet = trySet.union(set(unitNbrs[U] ) )\n",
    "        tryList = list( trySet.difference(set(addedList)) )\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP and wontEnclave(UU, addedList, unitNbrs, borderUnits) :\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    smallPieceList = foundList.copy()\n",
    "    if len(foundList) < len(VLIST):\n",
    "        isUnbroken  = False\n",
    "        if len(foundList) > 0.5*len(VLIST): #return a contiguous sub-list that is guaranteed to have at most half the total units in the list\n",
    "            smallPieceStarter = list( set(VLIST).difference(set(foundList) ) )[0]\n",
    "            smallPieceList = getContigFromStarter(smallPieceStarter, VLIST, NEIGHBORLIST)\n",
    "    return isUnbroken, smallPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  GA\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use ga_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. GA has 10711908 peeps and is divided into 2698 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 14 14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 159 counties in GA .  I will assign them county Nos 0 - 158\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Building county geom for county 80\n",
      "Building county geom for county 100\n",
      "Building county geom for county 120\n",
      "Building county geom for county 140\n",
      "Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 1 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 1 units will be axed\n",
      "here is the proposed skipList [1466]\n",
      "here is the final skipList [1466]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [] #...  #alter here if needed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 24\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.84149\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 1004 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1641 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland = [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "        plotPoly(mainCountyGeom)\n",
    "        plotCenter(c,mainCountyGeom)\n",
    "        for t in countyTractList[c]:\n",
    "            if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                if tractGeom[t].intersects(mainCountyGeom):\n",
    "                    print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                    plotPoly(tractGeom[t],0.2)\n",
    "                    tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                    tractCP[t] = tractGeom[t].centroid\n",
    "                    tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                    plotPoly(tractGeom[t])\n",
    "                    plotCenter(t,tractGeom[t])\n",
    "                else:    \n",
    "                    isIsland[t] = True\n",
    "                    print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                    islandList.append(t)\n",
    "                    if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                        isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                        isleAreas = [geo.area for geo in isleGeos]\n",
    "                        biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                        tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                    p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                    islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                    plotPoly(tractGeom[t])\n",
    "                    plotCenter(t,tractGeom[t])\n",
    "                    plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                    tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                    tractCP[t] = tractGeom[t].centroid\n",
    "                    tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                    plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This is not appropriate for large islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This is not appropriate for large islands like Michigan UP\")\n",
    "for c in range(nCounties) :\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block identifies whole districts to be cut out of the map\n",
      "For GA my input file says to cut out 0 districts out of 14\n",
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# updated NCUTDISTRICTS (district slice-out) code vs. vanillaRefine - this WORKS\n",
    "print(\"This code block identifies whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "print(\"here are your cut districts\")\n",
    "plotPoly(MAP)\n",
    "for i,L in enumerate(allCutLists):\n",
    "    cutGeo = tractGeom[L[0]]\n",
    "    for v in L:\n",
    "        cutGeo=cutGeo.union(tractGeom[v])\n",
    "    plotPoly(cutGeo,2)\n",
    "    plotCenter(i,cutGeo)\n",
    "    plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02f95c46-5073-4b9d-817c-16a57d5ebb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before skewing outcroppings.\n",
      "Of the total statePop 10711908.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 1 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 14 rather than original 14\n",
      "Each district will be drawn to house 765136.286 = 1/ 14 of non-cutout state pop 10711908.0 vs original= 10711908.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "working on county 80\n",
      "working on county 100\n",
      "working on county 120\n",
      "working on county 140\n",
      "here is the GA map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before skewing outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 12\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "Working on county  80 distances\n",
      "Working on county  100 distances\n",
      "Working on county  120 distances\n",
      "Working on county  140 distances\n",
      "the max LAT-scaled distance between counties is 5.20787\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 5.20787  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "Working on county  80 neighbors\n",
      "Working on county  100 neighbors\n",
      "Working on county  120 neighbors\n",
      "Working on county  140 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE- do not use clipLines to find outcroppings.  Instead, find boundary counties with low neighborcounts\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d63e9f85-336f-4e38-8ed7-75035e9845f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.125 of 765136\n",
      "checking if county 40 with pop 16251.0 and only 1 neighbor is less pop than 95642 vs districtPop 765136\n",
      "checking if county 22 with pop 67872.0 and 1 or 2 neighbors is less pop than 95642\n",
      "checking if county 26 with pop 24965.0 and 1 or 2 neighbors is less pop than 95642\n",
      "checking if county 118 with pop 16883.0 and 1 or 2 neighbors is less pop than 95642\n",
      "checking if county 40 with pop 16251.0 and 1 or 2 neighbors is less pop than 95642\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [22]\n",
      "1   [26, 145]\n",
      "2   [118, 67, 138, 5]\n",
      "3   [40, 145]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 765136.286 95642\n",
      "let's decide whether to delete, accept, or modify list [22] with pop 67872.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [22, 145, 40, 26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [22] with [22, 145, 40, 26]\n",
      "let's decide whether to delete, accept, or modify list [26, 145] with pop 92619.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [118, 67, 138, 126]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [26, 145] with [118, 67, 138, 126]\n",
      "let's decide whether to delete, accept, or modify list [118, 67, 138, 5] with pop 93442.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [40, 145] with pop 83905.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n",
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [24]\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "176742.0 [22, 145, 40, 26]\n",
      "102191.0 [118, 67, 138, 126]\n",
      "295291.0 [24]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copied 2/17/24 from CO\n",
    "\n",
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./8.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15302\n",
      "We defined a total of 52 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "138 fragmented VTDs out of 2698\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 8000\n"
     ]
    },
    {
     "data": {
      "image/png": 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VMxKy18li8Z82AQDuOmUmLmqK4N49B/CDbT0AgK76IAwh8PRA/kqlvRcsVhWVFEkktiEYnK06BlHF4PLqRWaaDchmByf0GkZTE4RpqikeAGD6gY/dBxh+4I53A5tWAd7RFzl744tX4NZfP40pA4tRl24EAGSe+mfgB03Aga2lTD1pWiwThgDmBCwsm9qAgK7hyrYmrF4yD2e4Q3huIIb9qfwGZLOgwUtxMapaIwR/PRJNlur4ZyyNzSl/Acw4B/jtPwL3fw4ITQNmnpv/b/IAcMpfYcicicfkl9CZBDq3ALsjb+CVBf+B5gu+C7x4L3DHOcAV9wILP6L6u5mwT7U24YF9A7h9xz58eGoE5659PX+HHgEAvJF2MC3l4e4/xxFHD8IXzFCYloioerB81Jq6FuCTvwL2vAi8+iCw95X8h5dDbMP/4v/tWIx0Jn+Vi9Bb4bWE8dsP/QLimR8BdiMwsAPY8ruqKB83ntSKwayLW7p7cUt378jtTYbEDzetgTVwNubGPFgeUHdeh8KkpIKUkpfeEk0Slo8x8vmmwHH2w7Kmqo4ycUIA7WfmPw7JZfC/f/9R9GsBpObOAaSH4LZN+F7uQYg77hn9/Lrq2Iq8wTRw96mz8JmXt+OPB6Mjt/8yGkLzjjNx+Oq2XjwLXdUicKSEbc9AMrkNweAc1VGIqg7LxxgZRhCZTJ/qGEX3m9/8Bi+++CIsy0J718eQ3L595L74ye/CrXgXvoz/xDrfqZj9mRWY3dIImGNfcK0S3DSnDc+/GEfMzZeN50+L4ENP7x2531sylcWjBmmaASH4K5JoMvBvVo3bsGEDAMBxHGw7rHgc7ux3r0TcH8C3omlcO7OuhOlKY27Qjw3nnoK7dvVhqs/EZ1obgZubAQkIg5MOa5lpRpB29sJvtaiOQlRV+Ju1IPlzwNXk29/+Nurs0FHvmyaacLbxPiQtGwDQNaO9lNFKKqjruG5WCz7b1gQhBISusXgQTLMBbi4Jz+PVTkTFxJGPAvj9bUine2Db01VHKZrs3gQ+MfBuuPCwXwzhUd96nJHrxCluB3zDPx7nHHBx2hmtWBKprtMtRGMRCHQimeyGEAK6HqyOeV9EirF8FEDTLEiZUR2jqPb9+3oAgA4NrbIBVzmj95bJ2jru+OApmBaexJVXicqYEALB4EkAgFwujkRiG0wzAp+vWXEyosrFceWCVddld63fOPu49zcsaUFLxOblhkQADCOEYHA2stkoPM9RHYeoYrF8FKy65nzodT5Mv/lcaMe4miOwmJtrEb1TMHgS0ukepFK7VEchqkgsHwXy+ZrhONV1ya3QNbRef9ao20Lvb0fb998DX3v1Xd1CVAyBQCdMswGp1B7VUYgqDud8FMgw6pDJ9AOorhEBYWpou6kL0nGh1fkgNJ5mITqRnBuHYXAiNlGhOPJBIzS/AT1isXgQjZHfaoHnOYjFXoPnVddkdKLJVFD5WLFiBRYtWoRwOIxwOIyuri48+uijR33sF77wBQgh8OMf/7gYOYmIypJlTUNd3QIkk91w3bTqOEQVoaDy0d7ejltvvRXr1q3DunXrcOGFF2LZsmXYtGnTqMc99NBDWLt2Ldra2ooatlwc2ueFiOiQUGg+HKcX6XSP6ihEZa+g8nH55ZfjQx/6EObNm4d58+bh5ptvRigUwnPPPTfymD179uDaa6/FL3/5S5imWfTA5cAwgnDdpOoYRFRmAoFOXoJLNAbjnnDqui7uv/9+JBIJdHV1AQA8z8Py5ctx/fXX45RTThnT6ziOA8d5+y9rNBo9zqPLSXVdcktExaFpfuRyMRgGrxQjOpaCJ5xu3LgRoVAIlmXhmmuuwapVq7Bw4UIAwD//8z/DMAx8+ctfHvPr3XLLLYhEIiMfHR0dhUYiIiobfn8rHGcfT78QHUfBIx/z58/Hhg0bMDg4iAceeABXXnklnnzySaRSKdx+++146aWXCloN84YbbsB111038nU0Gq2IAmJZ0+A4+2BZ01RHIaIy4/NNQS43pDoGUdkScoLbtC5duhSzZ8/GggULcN1110HT3h5McV0Xmqaho6MDO3bsGNPrRaNRRCIRDA0NIRwOTyTapEsmuxEIdKqOUVGklBBC4NUDr+Kejffgjzv/iLuW3oX3TH+P6mhEReW6SWQyB2Hb1bsbNNHhCnn/nvA6H1JKOI6D5cuX45VXXsGGDRtGPtra2nD99dfjd7/73UT/mDLF9TDG6q2Ne3D3VfdizXlXYndsF374x4/j4LaNmBGbgesfuR53v3I3kllO4qXqoesBSJkbXpQQ8LwMPC+nOBVReSjotMuNN96ISy+9FB0dHYjFYli5ciXWrFmD1atXo6mpCU1NTaMeb5omWlpaMH/+/KKGLieH/iVPx/f//s8XAACbZs5F9s8X4OopEpiyAy88vwzpA2Ec/MVWbH30HET/51/wvgWXKE5LVByBwCxkswNIJndA03zwPAdC6LDtGaqjESlV0MjHvn37sHz5csyfPx8f+MAHsHbtWqxevRoXXXTRZOUra5Y1DU5mn+oYZW//ju0jnzuZrXj53oXo7pkJAKif0wIASAUCWPXRv8Lun67AqlWrMMGzgURlwzQbEAjMgt/fNrwfTBOSyW7VsYiUmvCcj2KrpDkfAOd9jMV/f+dr6Hlj86jbQiclEGs6FfX1+9B50ovY/sa56BucBi+jIyf9eN/73ocLL7yQo0pUlbLZATiZAwjYs6Bp1bkeEtWeQt6/WT4miOXjxDLpFJ74r7uRyzjY170NDS2tOO2iy/Dm/gMwfX836rH9W+Zj896zAQDnn38+zj//fAWJiSaflBKp1A5IKREIdLJoU8Ur5P2bu9oWAed9vG3Lv9yDXWfegrrQKVi48DaEQvPh89v44DWj137J5WJ466lPHPH8Qbz9A9sW5mXMVL2EEAgEOiGli2RyO4TQYNszIISuOhrRpGP5mCDLaoXj9MLvr859bArheB7ef9aZuFpehAvjf8Da5z+EKVM+iBkdVyEcXgxNy/+4ZbMDeOrpM0c9d9q0ZQj//COAvg+W1o+u7Dx0BFg+qPoJoSMYnA0pPaRSbx26FVJ6CARmsoxQVWL5mCBd949aHr6WSQlYMgUNLiKh92J6x19g82tfRV/f73D6onvQ3HwBAMAw6qHrARhGGHWhheg/+AzaWj+KwFULYT+5C/O3DaHu/A7YJzcq/o6ISkcIbdQp3PxpmbcgZQ62PWukvBNVA/40U9H4dQ1vvGcmnv3T4xiKA073W9A0C6bZgKam9488TgiB88/beOQLNAL+eQ0lTExUvvKnZWaNlJD8flKHTs3wNC9VNpaPIuG8jzy/vw3vPfc5DAw+h1h0I7LZAbS0/AWHjonG6VAJAQDPyyKZ3AYhDNj2TP7OoYrFq12KwHUdZLP9nPdBRCWRLyHb4fe3cfdcKhslXV6dAF234Hmc90FEpaFpJkKh+XDdFNLOXtVxiArG8kFEVKEsayqymX4WEKo4LB9FI7gkOBGVXF3dKTD0OsQTW5HNRlXHIRoTlo8i8ftbkU73qI5BRDXIMIIIBefC8xzE41vgeRnVkYiOi+WjSDTNgpT8C09E6ljWFASDc5EcWayMqDyxfBARVREhBDTBVRSovLF8FBnnfRCRaqbZiEymX3UMomNi+Sgiv3860uk9qmNQKW1/Eri5DfjvT43c9P3fbMZXVq6H57GIkhq6HkAmc0B1DKJj4thcEWmaD1JmVcegEnr511fgp40RPJV5FbjvNJza+C78+dkrAGh4aEMPLlvUir9cPB1LF3KTPCodTTNh2zMQi72GUGgeVximssORD6ICHUgdwGn3nYarf3c1PtvWgqcC9sh9rx58Cb6mp0a+/t9XevG3P1+nIibVOF23EQqdjHhiq+ooREdg+Si6/FbYVJ1cz8UF/5Pfnff5vc8f9THnnLoPX7tk/sjXHz+zoyTZiN5JCIGA3YlEYpvqKESjsHwUGed9HCnrVk8Z08SJ/8p0RjrxpfPn4FuXLUB9wMS1F84pQTKio9N1C2IMP7dEpcSfyCLTNBNS5lTHKAuZnIfl/7EWc7/5KM7+8s/xyNkX4kcfvRa/W7cdyUxlHiMhBH7/0d9jVnjWqNs/Mf8T+fsh8PWzvg4A+Pz7TsKG71yM9oZAqWMSjSKEjlwupjoG0QjuajsJksluBAKdqmMoI6XEHzbvw9/+/MWR2x596Ksjn/vCWcQ/FMbpN72kIl7R/G7H7/DVJ7+KX3/411jYtBBA/nvnNudUjtLpXrhuEoYZgeVrVh2HqlAh798sH5MgmdwB255Rk0Odd3z6k+gInoy6yJnwGSHMwNuz7GO/uRZwM5iyKIrmhXH859INuOq9tVvSiFTI5WJIp3tgWa0wzcr8HUvlqZD3b15qOwkOzfuw7dqaaCilxAfaliNoHP2Hzpl5Ov6/BSbmLViAm96cjXO29LF8EJWYYdQhFJqPtLMX2WT/8GidBtdLI2B3Qtct1RGpBrB8TIJanPchcx72fOvZkeLR+IVFOHjXKyP3Owss/Ovi0/HMhpOBN/O3PbmlD0+8sR8XzJ+qIjJRTfNbLUfclkrtGdmjyjSbODJCk6b2zgvQ5HjHPIfDi8f+KQH4z5mLX3zyn3DX8iWjHrfy+Z0liUdEJ2bb0xEIdCIQ6ITnpZBIbEc2O6A6FlUhlo9JU1vrfQj92JMs/7x1CA/9aD0A4IOntODpr12AK5a040efOB0/+fS7ShWRiApgWdMQDJ6EXC6JVIrLB1Bx8bTLJPH722tu3ocW9sGLZkbd9sdofrn5pvbQyG0djQH868dOL2k2Ihof256OXC6GRGIbLGsaDCN04icRnQDLxyTRNKPm5n20Xn8mZE4iuS+J1zf3Y/uGA4gPZnH60g6894q5quMR0TgZRh0Mow7JZDekzME061VHogrH8kFFI0wdwgRCs8I4c1YYZ36IV7IQVZNAoBPx+BYYRrgmlxKg4uFPz6TSamreBxFVv0DgJCSTO1THoArHkY9JlF/vYzdse4bqKERERaFpBkwzPFxAJITQ4fd3cGVfKgjLxyTKz/twVccgIioqn68ZvuEl2j0vi1RqB6R04fe3Qde5lxGdGE+7EBHRuGmaiUCgE8HgHKRSu1XHoQrB8jHpNI5+EFFNMM0GpNO9qmNQBWD5mGS2nV/vg4iomsViryGbHYDf36o6ClUAlo9JJoTOkQ8iqmrJZDc0zYLrxhGLbVIdhyoAJ5wSEdGEaJoFIXQEgyepjkIVgiMfJcDRDyKqZn5/G7LZIUgpVUehCsHyUQL59T4474OIqtPg4DoIoSGXG1IdhSoET7uUAEc+iKiaWVbr8OJj9aqjUIVg+SAiognRdRu5XBRDQy9BQiJcdyo0zVIdi8oYT7uUCEc/iKha+XyNCARmIRCYA8/LoL//SWSzPAVDx8aRjxLx+6cjldqNQGCm6ihERJPCNMMIBubAsqZgcHAdfL4meF4GPl8zNM0PwwiqjkhlgiMfJSKEDoA73BJRdbOsKQCASGQJAMDnm4JEYivSaS69Tm9j+SAioqITQiAQ6ISmWTDNRm44R6OwfJSQEAY8L6c6BhFRyRhGEKHQPGiaH0NDL6mOQ2WC5aOEuN4HEdWqWHwTstkhZLNR1VGoDHDCaQkJoYHzPoio1mSzURhGHepCp0DX/arjUBlg+SAiokllmmHUR5YgldoDz3NgmhH4fE2qY5FCLB8ldmjeh6bx0BNRbbHt6QCATOYgYrHNsO0OpFL5q2BCoXnDVwVSLeCcjxLjvA8iqnU+XyOCwbnwvAxMM4JQ6GTE41tUx6IS4j+/S4zzPoiIAE0zR0695HIJaJqpOBGVEkc+lBCqAxARlY2hoZdg2x2qY1AJsXwoIITO9T6IiIZFIouRTveqjkElxPKhAOd9EBG9zTDq4PM1IZ7YCtdNq45DJcA5Hwpw3gcR0WiGUYeQUYdUahc8LwO/vw26bquORZOE5YOIiMrGobkf6XQP0ukeCKHD52uCYdQpTkbFxPKhiBAmPC8DTfOpjkJEVHb8/raRzx2nD5nMAQCAYUTg8zWqikVFwvKhiN8/HanUDgQCnaqjEBGVNcuaAmAKgPwCZclk98h9npdBIDALmmYpSkfjwQmnigjBy22JiArl8zUiEOgc+QAEDg78mRNVKwxHPpRiASEimohQaB4sqwWOsxfR6MtoaOiCz9fEpdrLXEEjHytWrMCiRYsQDocRDofR1dWFRx99dOT+m266CSeffDKCwSAaGhqwdOlSrF27tuihq4WmWWzrREQTZJphBAKz0NR0PqLRlxGNvoJkshtSuqqj0TEUVD7a29tx6623Yt26dVi3bh0uvPBCLFu2DJs2bQIAzJs3Dz/5yU+wceNGPPPMM5g1axYuvvhi9PX1TUr4Spdv61xYh4ioGEwzgilTLkIgMAuW1YpEYhti8ddVx6KjEFJKOZEXaGxsxG233Yarr776iPui0SgikQgee+wxfOADHxjT6x16ztDQEMLh8ESiVYRkspuTTomIJonnOejtfRCtrVdw/5hJVsj797jnfLiui/vvvx+JRAJdXV1H3J/JZPDTn/4UkUgEp59++jFfx3EcOI4zKjwREVExaJqFqVMvRSy2EUIYCIcXqY5EGMfVLhs3bkQoFIJlWbjmmmuwatUqLFy4cOT+3/72twiFQvD7/fjRj36EP/zhD2hubj7m691yyy2IRCIjHx0dtbW5kG7UIZeLqY5BRFS1TLMeweB81THoMAWfdslkMti5cycGBwfxwAMP4J577sGTTz45UkASiQR6e3tx4MAB3H333Xj88cexdu1aTJ069aivd7SRj46Ojpo57QIAyeQOBAKzVMcgIqpqicQ2BIOzVceoWoWcdpnwnI+lS5di9uzZuOuuu456/9y5c3HVVVfhhhtuGNPr1dqcD4DzPmiM3BwgNEDj8jxEhXCcPjhOL2x7BkyzXnWcqlWSOR+HSClHjVwUej8RjcHqG4Dn7sh/Xj8T+PC/AXOWqs1EVCFMMwzXTbB4lJGCyseNN96ISy+9FB0dHYjFYli5ciXWrFmD1atXI5FI4Oabb8ZHPvIRtLa2or+/H3fccQd2796Nj33sY5OVvyqYZgOy2QGYZoPqKFRuor3A2hVvF4/TPg68+QfgiVtYPojGKL+mUlJ1DDpMQeVj3759WL58OXp7exGJRLBo0SKsXr0aF110EdLpNF5//XXcd999OHDgAJqamnDWWWfh6aefximnnDJZ+auCadYjmexm+aC3vfQzYPPDwPY1gOEH3vP3wLu/CESmA3++A/jDt4HBnUD9DNVJiSpCINCJRGIbTDMCn+/YF0FQaUx4zkex1eKcD4CTTukwbg74QVP+8/f8PfD+6wHPBTb8EnDiQLAZeOSrgNCBxZ8CLv93zgMhGgMpJQaH1qGh/izVUapSSed8EFGR6Qbw6f8BHvoi8OLPgNg+YOefgaFdgC8EZOL5x0kXWP+LfDlpmKU0MlElEELAb03DwOALCAQ6YXEERBn+c6lM+HzNcBwuQ0/D5n0Q+MJTwDnXAPs354sHgJebLgW++Geg61pg6kLgsw+weBAVwDAiALitp2o87VJGeOqFjsU7sA0PrvgWfF1/g48svVB1HKKKJaVEOt0DoemQXha2XVsLW06mkq7zUWy1XT66AeT/cgQCnRCC3ZyIaDK4bhKZzAD8/hYIoauOUxU456NCHVpoLJPpRzq9C7bNKxmIiCZDKr0HUrqIxfqh6yH+g6/EWD7KTCbTD89zWDyIiCZRKDh35PN4fAvS6T2w7XaFiWoLJ5yWGSH0/BLaRERUIhKp9C7VIWoK3+XKjGnWI5eNIpeLq45CRFQzwnWLVEeoKSwfZSgUmodcLoZYbDM8L6M6DhFRVXOc/Uin96iOUVNYPsqU398Kv78dAwPPIZeLqY5DRFTVUqmdqiPUFJaPMpZ2etDU9H4YRp3qKEREVau+fglMsx7R6EbVUWoGy0cZO7ThXCbTrzoKEVHV0vUAdCMEz3NUR6kZvNS2jPmtFgBAPLEVuh6ErvsVJyIiqk6aMGBY01THqBkc+agE0mPxICKaROn0Hmgaf8+WCstHBRDCVB2BiKhqxeKvw+9vh2VNUR2lZrB8VAAhBMpsCx4ioqpRFzoZuh7A4NCLqqPUDJaPCmDbM5BMdiOd7lEdhYioKvn9rbD97chmB1RHqQksHxVACB3B4EkQwkAmc1B1HCKiquRk+pDNDqqOURNYPipENhtFLheFplmqoxARVSXb34FUinu8lALLR4UQQsB1k0gmt6mOQkRUdVKpndi772E0Nr5PdZSawPJRIQyjDuEwNz4iIpoMmmajuekCCCFUR6kJLB8VJhDohOP0qY5BRFRVLGsKNM3HPV5KhOWjwhhGHbK5QdUxiIiqjmVNhWk2IR5/A8lkN1w3rTpS1WL5qEB+qw3x+BuQ0lMdhagsvf7663jqqacwODioOgpVGMMIIhSaD9uehYHB5xBPbFUdqSpxb5cKZBhBBINzkUhug+WbAtOsVx2JqGxIKfHII48gGo3i8ccfx3vf+14sXbpUdSyqMEIINDedj2SyG8lkNwDAslrgeQ5yuRh0PQCfr0lxysrFkY8KJYSGUHAuXDeJVGqP6jhEZSOTySAajY58/cwzzyAejytMRJUsEOhEINAJ256FbG4IACCEgWhsI2KxzYrTVS6Wjwrn97dB120MDW3gaRiqeVu2bMGvf/3rka+vuOIKAMD999+vKhJVCSEE/FYLTLMefn8rmpvORzY7wN+748TTLlXA52uEadYjGtuIutAp0DT+b6Xak8lk8Ktf/Wrk65NOOgnPPvssAGBoaEhVLKpiDQ1dSCa7IYQB06yHYdRBCP6bfix4lKqEEBrCdYuQTG5XHYWo5BzHwQ9/+MNRt23fvh29vb0AgJaWFhWxqMoJoSEYnA2/vxVSZtHfv0Z1pIrBfyJXkfziONz9lmpPLpcb+XxerhVn5DrRuKAFfUs0BINBdHZ2KkxH1U7TfPD5mhEKLUDa2Qu/xbJ7IkKW2V7t0WgUkUgEQ0NDCIfDquNUHMfZD10PwjCCqqMQlZSUEkNrdyH+0FsAgKl/txi+jjrFqajWZDIHkc0eRCAwu+ZWSy3k/ZsjH1XG87IwDF11DKKSE0Kg/pwZCJzSBF8dyzep4fM1wjDCSCTfhCZM2HYHhODv5HfinI8qo2kmXJeXFVLtYvEg1TTNgM9sRCq1C56XUR2nLLF8VBnLmgoASCZ3cP0PIiJFotGXUV+/BLpuq45Sllg+qpDP14xAYBakzMDzHNVxiIhqjm3Pgq4HVMcoWywfVSqd7gEAaJqlOAkRUe0xzDBHn4+D5aNK5XIx+P1tqmMQEdUky9cMXbeRSu1UHaUssXxUofyoh0A63as6ChFRzfL5GiGlhOsmVUcpOywfVUjXbThOLwyDaxwQEalkGMGR0+D0NpaPKpTJDCAcPp3bPRMRKebzNcP10hyJfgeWjyoipYtYbBNsux2mWa86DhERAQjXnYpMpg99fY8hGt2IbJYbHXKF0yqSTu9FMDgHmuZTHYWIiA5TV3cqwmENjtMHJ7MfphlRHUkplo8qYtvTkUy+hfzmchKm2VjzP+BEROVAiPyJBp+vEZnMAcVp1GP5qDKBwMyRzzOZfiST3QDy633w0lsiIrWE0CE0A+l0T03/Tuacjyrm8zUhEOhEINAJw4ggHt+CXI77vhARqRQMzEE2F1UdQymWjxphGEGEQvOQy0WRSGyDlFJ1JCKimpNO92BwcC1s/3TVUZRi+agxfn8bfL4pOHDgMe62SERUQrlcHOl0DyKRd9X8Okyc81FDUqmdSKV2we9vx5QpF6mOQ0RUM6R0EY+/jkjkXSOTT2sZy0cNMYwIbBuw7RmqoxAR1RgBXQ/C87LQdW74yfJRpVzXgePk93g5xPMchELz1YUiIqpRQmgQQkMisQV1dadCCHHiJ1Uxlo8qlUq9BU0z4fdPh6aZquMQEdW8UGg+PC+DePw1hELzIYSuOpIyPPFUhVw3BU0zIaWE56VVxyEiomGa5kMotAD79z9a01cdcuSjSkjpIZncDiF0aJoftj2r5of1iIjKkRACPt9UZLMHa3YDUJaPKpDNDsHJ7EcwMJuzqImIylw8sRWmrwG6bquOogzfqSqc4/Qhl4shFJzL4kFEVAFCwbnw3DSk9FRHUYbvVhXKdVNIJLZBCAHbblcdh4iICqDrARhGSHUMZVg+KpYGTfPD52tWHYSIiKggnPNRoXTdguc5qmMQEdE4+HzNw7uO5xcfs6wpqiOVFEc+KphhBJHN1vbOiERElcg0I8O7js+C6yaRTHYjne4Z9Zi0s7dqL8flyEdFE8jlYjDNsOogREQ0ToHATAAYKSGHGEYYqdQOCKFX3bYYLB8VLJeLIRicrToGEREVga4HEAh0jrrNNBsxOLgWptlYVRNUCzrtsmLFCixatAjhcBjhcBhdXV149NFHAQDZbBZf//rXcdpppyEYDKKtrQ1//dd/jZ6enhO8Ko0XL60lIqpuQgg0NJwD100hGn0FnpdVHakoChr5aG9vx6233oo5c+YAAO677z4sW7YM69evR3t7O1566SV8+9vfxumnn46BgQF85StfwUc+8hGsW7duUsLXOpcTTomIaoJlTYHP14hkcgcMIwTLmnbEY6T0EIu9CsMIA3h7rojP1wzDqCth2hMTcoKzWRobG3Hbbbfh6quvPuK+F154AWeffTbeeustzJgxtvNV0WgUkUgEQ0NDCIc5l+F4otFXEAot4MZxREQ1JJsdQDY7CNOsh2k2jLovkzmAXC4GKb2R0/KO0wfXjUPTLPj9bZOWq5D373GP27uui5UrVyKRSKCrq+uojxkaGoIQAvX19eP9Y+gYpPQgpVfTuyISEdUi02xAINAJx+lDMvkWACCXS+DAgSeGr5gRsKypI4+3rCkIBDqh6/aoCa0qFTzhdOPGjejq6kI6nUYoFMKqVauwcOHCIx6XTqfxjW98A5/+9KeP24Acx4HjvH36IBrlpaNjkUhsRTi8iPM+iIhqVCg0D7HYJjjOfqRSOxEMzoNtTz/m402zAZrmRzz+BkKh+SVMeqSC37nmz5+PDRs24LnnnsMXv/hFXHnlldi8efOox2SzWXzyk5+E53m44447jvt6t9xyCyKRyMhHR0dHoZFqjpQSmuZj8SAiqnG2PQO5XAyRyJLjFo9DdN2GpvlKkOz4JjznY+nSpZg9ezbuuusuAPni8fGPfxzbt2/H448/jqam428XfLSRj46ODs75OA4pJRKJrQiF5qmOQkREFSaZ7D7ikt5iKGTOx4TX+ZBSjpSHQ8Vj69ateOKJJ05YPADAsixYljXRGDUjv5mcjkDgJNVRiIiowjhOX1lc+VJQ+bjxxhtx6aWXoqOjA7FYDCtXrsSaNWuwevVq5HI5XHHFFXjppZfw29/+Fq7rYu/evQDyV8T4fOqHeaqBrtvDw2ZcH46IiMbG8xykUrthGCH4fEdepltqBb2D7du3D8uXL0dvby8ikQgWLVqE1atX46KLLsKOHTvw8MMPAwAWL1486nlPPPEEzj///GJlrllpZy9cNzWpl0oREVF1SaV2wfOyZbUi9oTnfBQb1/k4kpQSyeQ2mGYDfL4Tn8oiIiLKlw4Hfn8HdH3ypzeUdM4HlYbrpRFk8SAiojFIJrthWS3QdVt1lKNi+ShzA4MvwPI1I2DPUh2FiIgqQDrdC8OIlG3xAFg+yp5p1k/KJVFERFR90ukeCGHC52tUHeW4WD7KWCq1E36rVXUMIiKqAIeuZjHNetVRTohLZJYxz8vAMEKqYxARURmT0kMi8SZ03a6I4gGwfJQ5gVwuoToEERGVKSklYrFXEQicVFFXQ7J8lLFA4CQ4Tq/qGEREVIY8L4d4/DXU1Z1ScXt9cc5HGRNCQAhddQwiIioTUkpksweRy0UhpUQotABCCNWxCsbyUcay2UHoekB1DCIiKgNSSkSjLyMQOKnir4KsrHGaGuM4+6Bp5XudNhERlU4isQXh8Gkwzcpf/Zvlo4yFQvORSu9UHYOIiBTyPAfx+Buw7ZlVcyqe5aPMmUYErptSHYOIiBRJp3sQDM6BrvtVRykalo8yZ9sdSKd5xQsRUS3KZA5ACKNqRjwO4YTTMpfLJaBpk78bIRERlYdcLo5M5gCkzMEwwrDtDtWRio7lo8xlMn0IBGapjkFERCUwNPQS/PaM4fkdlXcJ7VjxtEuZ0zQLjtOnOgYREZWAaTbBZzZWdfEAWD7Knt/fCiF0pFK7VEchIqJJ56kOUBIsHxXA52uEYdQhmexWHYWIiCaJ6yYhhF5xS6WPR/V/h1XCNOthWS2IxV5D2tmrOg4RERVZKrUbtj1DdYySYPmoILpuQwgNhh5SHYWIiIpM00zVEUqG5aPChELzkc0OIpHYDs/Lqo5DRERUMJaPCmTb7bDt6Uind6uOQkRERZDJHIBp1quOUTIsHxVK0yxIKVXHICKiIsjlYjDNBtUxSoblo4JV+3XgRERUnVg+KhhHPoiIKl82OwTDiKiOUVIsHxVMCA2um1Qdg4iIJiCbPQifr1F1jJJi+ahggcAsrnxKRFTxau8UOstHxau9H1oiomohpQfPy6iOUXIsHxXMdZ2aWpSGiKiaSOkhHn8NweAc1VFKjuWjgqVSOxAIdKqOQURE45BKvYVQaEFN7OXyTrX3HVeJVGonbHum6hhERDQO6XQPNM2qyeIBAIbqAFQ4z8tASgld96uOQkREYySlHNmd3OdrhmmGFSdSh+WjAknpQtMt1TGIiKgAyeQ2BAKdEEJXHUW52hzvqXC6bsNzU6pjEBHRGKXTvTDNRhaPYSwfFSibHarJS7OIiCqV66ZqbiGx42H5qECZTD9CofmqYxAR0RgkEtvh97eqjlFWOOejAnFDOSKi8ue6KaRSu+D3T4eu26rjlBWOfFQY101DCC4sRkRU/jRIeCweR8HyUWFS6V2w7XbVMYiI6AR03UIoOA/J5HbVUcoOy0cFyWajMI161TGIiGiMhNB4hctRsHxUEF23kc0eVB2DiIhoQlg+Kgg3kSMiqiyH1veg0Vg+Koym+VRHICKiMXLdJEwzojpG2WH5ICIimiSc73F0LB8VJJeL8zJbIqKKIlUHKEtcZKwCuG5yePtlm5fZEhFVFC4KeTQsHxVACAO6HoDf36Y6ChER0YTxtEsF0DQfcm5CdQwiIioYT7scDctHhTCNemSzg6pjEBHRGCUSb8KyOGJ9NCwfFcKypiCT4QJjRESVIJXaCcuaBl23VEcpSywfFcLzHNURiIhoDDKZfmiaH4ZRpzpK2WL5qBCp1B4EgyepjkGTJOtl8dbePfC8I88PH+02IipPnpdDNjsAy5qqOkpZ49UuFULX/XAyB2D5mlVHoUnwwZ9fho/++RsA3sBJZ0xBNp2D50nseWMQABBqtHDlD89VmpGITiyZ3IZgcJ7qGGWPIx8Vwu9vQ44TTqvWv136LyOfb1/fh12vDYwUDwCIH3SQSecUJCOisfC8HOLxN+D3T4cQXNvjRDjyUUFMswHJZDeklDwFU2UWT12MP2lPQL7jFMv8d7egvyeOA7viuPsrTyE8xUagzsSSS2Zh1iKOghGp4HkOUqk9EELD25fSaggG57F4jBFHPiqIz9eEQKATtj0d8fgbquNQEf3h8/92RPEAgDMunoFPfPNstJ/cAACI9qWQGMrgf+94BdvX95U6JhEhv/CjEBoCgVkIBDqHP2ayeBSA5aMCaZoFy2qF4+xTHYWKZHfwtKPevuWF/P/jZV85Ax/8m1Nh+nXE+tMAgDee31uyfET0NiF0eF5GdYyKxtMuFco0w3CcFBKJbfD7p0PX/aoj0QR87sdLkXVcvPL4Lqx9uHvk9sbW4Mjnc5ZMxbTOMN5ctx+hBguzl3A2PZEqmsZNPieC5aOCWdY0WNY0xONbEApxdnUlE0LA5zfQNq8BQL58fPybZ6G5PTTqcXWNfpxx8QwFCYmIioflowr4/a1wnD5Y1hTVUWiC2ubU40srLuC5Y6Iy5rpJCKGrjlHRWD6qhJS8DLNasHgQlScpJdLp3ZDSQyAwU3WcisbyUQUMow7ZbJSjH0REkyCR2DY80iFhWW3cr6UICrraZcWKFVi0aBHC4TDC4TC6urrw6KOPjtz/4IMP4oMf/CCam5shhMCGDRuKnZeOwbanw3XjqmMQEVWVtLMXljVt5LJaFo/iKKh8tLe349Zbb8W6deuwbt06XHjhhVi2bBk2bdoEAEgkEjj33HNx6623TkpYOhGNl38RERWR56agaSwcxSaklBPataqxsRG33XYbrr766pHbduzYgc7OTqxfvx6LFy8u6PWi0SgikQiGhoYQDocnEq3mSCmRSGxBKDRfdRQioqogpUQ88QZMsx5+q0V1nLJWyPv3uOd8uK6L+++/H4lEAl1dXeN9GTiOA8d5e7v4aDQ67teqdUIIaJpPdQwioqohhEBd6GTkcjEkEttgmhH4uMHnhBW8wunGjRsRCoVgWRauueYarFq1CgsXLhx3gFtuuQWRSGTko6OjY9yvRURENBkMow7B4Gy4bhq5XEJ1nIpXcPmYP38+NmzYgOeeew5f/OIXceWVV2Lz5s3jDnDDDTdgaGho5GPXrl3jfq1aJ6VENjsAKV3VUYiIqpJttyOT2a86RsUr+LSLz+fDnDlzAABnnnkmXnjhBdx+++246667xhXAsixYFifzFIMQAuHw6Uin90BKyevQiYiKLJ3ugc/HJQ0masIby0kpR83ZILWE0GHbMwB4qqMQEVWVVGoPAMAwQid4JJ1IQSMfN954Iy699FJ0dHQgFoth5cqVWLNmDVavXg0AOHjwIHbu3Imenh4AwBtv5Ld9b2lpQUsLZwmXUs5NwHUdXpNORDRB6XQPXDcFy2qBYQRP/AQ6oYJGPvbt24fly5dj/vz5+MAHPoC1a9di9erVuOiiiwAADz/8MM444wxcdtllAIBPfvKTOOOMM3DnnXcWPzkdV7juVCRT3ZjgldRERDXP8zIIBmezeBTRhNf5KDau81E8npdDIrEFweBcbv9MRDQOUrpIpXZzDt0YFPL+PeE5H1S+NM1AKLQAyeR25HJcep2IqFDpdA/8/umqY1Qdlo8qJ4RAKDQfqRQvYSYiKpTnZaFp3IO12Fg+aoSUWdURiIgqius6PGU9SVg+aoTP14xMpl91DCKiiuE4PbBtrro9GVg+aoSmWcjlYqpjEBFVDK4WPXl4IqtG5HJRBAKdqmMQURXJZoeQyfQhmx2EEBpCoYXIuXFkM/3w+ZoqegM2TjSdXCwfNULXg8hkDsLna1QdhYiqRCL5JuojSwAcWu16LzwvjUBgNlKpHdD1uopc6NB10/A8B7puq45StVg+aoRlTUUy2c3yQUTjkt/NNQafrxlCCEgpoWv+kfuFEPD7W0e+DgbnIB5/A4CAppkQwoTPNwW6bo0sfiiEKPW3cUJSSiST3airW6A6SlVj+aghQviQzQ7BNCOqoxBRBclmhxCPv4ZgcC6Sye0QQofrphAKnXzc54VC8w97jSjS6V0QQgcg4MksspkBRCLvKqtLWVOptxAMzlEdo+qVz/9xmnS2PR3xxFaWDyIqiGlGoGl+mGYDfL4meF4OUrrDRWKsrxGGaY5e9VIGJKKxV+C6SVi+KRBCh2FElI7QZjIHYFlTAfAS28nE8lFjBASSyR3w+ZpgGHWq4xBRmUune+F5aXheBlJ6EEIbHqmY+NuHEAK2vx26HoSu50/hJBJvwvOcUadwpPSQTu+G6zkIBedO+M89nkhkCTKZ/XCcfdCNOlgVPGm2nLF81JhDw4n5XRrTsKwpihMRUTlz3QSCwTmTdrWcz9c06utgcA7Szl4kkzsAIH96JjuIbOYgAoFZSCTeRDA4B1K6w5NCA0XNI4SAZU0DkB8FyV/10lbUP4NYPmqW39+GVGoPUqldXESHiI4ql4shmx0s+Z/rt1qOeV8qtRuJxHbkclFIuAjYs+A4e2ENP+fwMuN5DjLZAXhueuQ2wwiP+bSOz9eMZDK/O3g5To6tZCwfNcy2p+cnkiW2QhM+AIc2OBYAJIQwoesWNM2Cpvm5zDBRjTGMOmi6/8QPLCHbbh/5XEoXicSbsO0ZkDIHQEcs/jo0YUJKF64bRyi0EJrPGikPjtOHZLIb+d9zh+Q/92QGmhj9e05KD05m33ELERWO5aPGmWbkmBNQPS8Lz3PgeWlks9Hhv9yHFxQc9vVECei6DU3zQ9ctCOHjvzSIFJNSws0lVMc4JiH0UVfUADhiUus75U8183SzaiwfdEyaZg6PdoQm/c/K/yslDc9LI5OJw/Myh+7B6H+hnPCVhv87tudomu+wkR2rrC75I1LNcXq5MjJNCv6mpbKQv8QuCCBYsj9TSgkpM3BdB66bRDZ78AR7ORRahPLyp6/80PUANM3PER2qGLlc7IiRBaJiYPmgmiWEgBD5OS2TyfOycN0UsrkheO5+TOxUlTji+ZrmGz5lFYCm8XQVFU8me5CTLWlSsHwQTbJDp69MHP9c9Hi8PXqTQjZ7cPh01eHl5J1l5fCv3zlv5+hfa7oFXbOHC46v6N8Dla9w3alIJLYiFJqnOgpVGZYPogp2+OiNadYX/fWllMOTjlPIZA7A87IobF7N8U5VyeFRm+DwKSleTVVuDKMO2ewmuK5TkRvEUfli+SCiYxJCDM9XyS+tXUyHj9pkMn2HTTJ+5+jMsT4/4hVHPtM0a7jU2Cw1E1Rf/270969Bc/MFqqNQFWH5ICIlJmvUJj9ak4HrJuA4+4YvER/5U1H4nJu3C4+u28OjNHbNXBklhEAwOA/JZDevfKGiqY2/PURUM/KjNdbwaYLibVCWLzWp4SujBg+7Mqqwy7uPfN0cDKNu5PSTEFpR8hZTfkHCQWQyB+DjXidUBCwfRERjkC81gaLvJZIvNWm4bgLZ7ACk9DCxK6Lk8Gmn0PAoTXF+zZtmPQYG1rJ8UFGwfBARKZQvNfmriYrh0GmneHwzcm4C9ZEzR3aMnahcLlqU1yFi+SAiqiJCCHheEn5/28jurMWQyfQjFFpYtNej2lZ+JxeJiGhCcrkkNK04IymHpFJvwbanF/U1qXaxfBARVRnbng7H2VvU13TdVFFfj2obywcRURWyrGlIJN6E66aL8np+f1tRXocIYPkgIqpKphlBMDgHqdRO5HKJCb9eLhcvQiqiPJYPIqIqFgrNQ9rpmfDrSJktQhqiPJYPIqIqlkrtgt9qndBrOM5++P3tRUpExPJBRFTl5IT3t0k7vbCsqUXKQ8TyQURU1Vw3BSHGXz6i0VcQsLmnCxUXywcRURWT0p3QfjFSujDNcBETEbF8EBFVOQnXdcb3TOnCyewvch4ilg8ioqoWCp2MZKp7XM+VUiIUnFfkREQsH0REVU0IHZZvKgYGni/4uZpmcH0PmhTcWI6IqMr5fI3Q9UUYGlqPcHgxACCTOYBcLgrPS0OI/FtBIHDSqCtjUqndyOaGVESmKsfyQURUA3Tdj7q605BIbIUQBixrCkyzAZqWfxuQUiKd3g0pc/C8LAAJv78dTTbX96DiY/kgIqoRmmYgFDr6HA4hBGy7o8SJqFZxzgcRERGVFMsHERERlRTLBxEREZUUywcRERGVFMsHERERlRTLBxEREZUUywcRERGVFMsHERERlRTLBxEREZUUywcRERGVFMsHERERlRTLBxEREZUUywcRERGVFMsHERERlZShOsA7SSkBANFoVHESIiIiGqtD79uH3sePp+zKRywWAwB0dHQoTkJERESFisViiEQix32MkGOpKCXkeR56enpQV1cHIYTqOBUnGo2io6MDu3btQjgcVh2nJvCYq8HjXno85mpUynGXUiIWi6GtrQ2advxZHWU38qFpGtrb21XHqHjhcLisf0irEY+5GjzupcdjrkYlHPcTjXgcwgmnREREVFIsH0RERFRSLB9VxrIsfPe734VlWaqj1AweczV43EuPx1yNajzuZTfhlIiIiKobRz6IiIiopFg+iIiIqKRYPoiIiKikWD6IiIiopFg+qsSWLVuwbNkyNDc3IxwO49xzz8UTTzwx6jFCiCM+7rzzTkWJq8NYjvsh/f39aG9vhxACg4ODpQ1aRU50zPv7+3HJJZegra0NlmWho6MD1157LfeLmqATHfeXX34Zn/rUp9DR0QHbtrFgwQLcfvvtChNXvrH8fvmHf/gHLFmyBJZlYfHixWqCjgPLR5W47LLLkMvl8Pjjj+PFF1/E4sWL8eEPfxh79+4d9bh7770Xvb29Ix9XXnmlosTVYazHHQCuvvpqLFq0SEHK6nKiY65pGpYtW4aHH34YW7ZswX/913/hsccewzXXXKM4eWU70XF/8cUXMWXKFPziF7/Apk2b8M1vfhM33HADfvKTnyhOXrnG8vtFSomrrroKn/jEJxQmHQdJFa+vr08CkE899dTIbdFoVAKQjz322MhtAOSqVasUJKxOYz3uUkp5xx13yPPOO0/+8Y9/lADkwMBAidNWh0KO+eFuv/122d7eXoqIVWm8x/1LX/qSvOCCC0oRseoUesy/+93vytNPP72ECSeGIx9VoKmpCQsWLMDPfvYzJBIJ5HI53HXXXZg2bRqWLFky6rHXXnstmpubcdZZZ+HOO++E53mKUle+sR73zZs34/vf/z5+9rOfnXCzJTq+Qn7WD+np6cGDDz6I8847r8Rpq8d4jjsADA0NobGxsYRJq8d4j3nFUN1+qDh2794tlyxZIoUQUtd12dbWJtevXz/qMT/4wQ/kn/70J7l+/Xr5r//6rzIQCMgf/OAHagJXiRMd93Q6LRctWiR//vOfSymlfOKJJzjyMUFj+VmXUspPfvKT0rZtCUBefvnlMpVKlT5sFRnrcT/kT3/6kzRNU/7+978vXcgqU8gx58gHFc1NN9101Emih3+sW7cOUkp86UtfwtSpU/H000/j+eefx7Jly/DhD38Yvb29I6/3rW99C11dXVi8eDH+6Z/+Cd///vdx2223KfwOy1Mxj/sNN9yABQsW4LOf/azi76q8FftnHQB+9KMf4aWXXsJDDz2Ebdu24brrrlP03ZWvyTjuALBp0yYsW7YM3/nOd3DRRRcp+M7K12Qd80rD5dXL2IEDB3DgwIHjPmbWrFl49tlncfHFF2NgYGDUdstz587F1VdfjW984xtHfe6zzz6L9773vdi7dy+mTZtW1OyVrJjHffHixdi4cSOEEADyk8M8z4Ou6/jmN7+J733ve5P6vVSKyf5Zf+aZZ/C+970PPT09aG1tLWr2SjYZx33z5s244IIL8PnPfx4333zzpGWvVJP1s37TTTfhoYcewoYNGyYjdtEZqgPQsTU3N6O5ufmEj0smkwBwxHwCTdOOO6dj/fr18Pv9qK+vn1DOalPM4/7AAw8glUqN3PfCCy/gqquuwtNPP43Zs2cXMXVlm+yf9UP/xnIcZwIpq0+xj/umTZtw4YUX4sorr2TxOIbJ/lmvFCwfVaCrqwsNDQ248sor8Z3vfAe2bePuu+9Gd3c3LrvsMgDAb37zG+zduxddXV2wbRtPPPEEvvnNb+Jv//Zvq2qnxFIay3F/Z8E49C+eBQsWsPSNw1iO+SOPPIJ9+/bhrLPOQigUwubNm/G1r30N5557LmbNmqX2G6hQYznumzZtwgUXXICLL74Y11133cjloLquY8qUKSrjV6SxHHMAePPNNxGPx7F3716kUqmRkY+FCxfC5/MpSj8GymabUFG98MIL8uKLL5aNjY2yrq5OnnPOOfKRRx4Zuf/RRx+VixcvlqFQSAYCAXnqqafKH//4xzKbzSpMXflOdNzfiRNOJ+5Ex/zxxx+XXV1dMhKJSL/fL+fOnSu//vWv85hP0ImO+3e/+10J4IiPmTNnqgtd4cby++W888476nHv7u5WE3qMOOeDiIiISopXuxAREVFJsXwQERFRSbF8EBERUUmxfBAREVFJsXwQERFRSbF8EBERUUmxfBAREVFJsXwQERFRSbF8EBERUUmxfBAREVFJsXwQERFRSbF8EBERUUn9/6niW3XYPzseAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 8000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 8000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 138 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "WARNING. vtd frag 100 in county 13 intersected county 91 but had no intracounty neighbors\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1000\n",
      "looking for surrounds, now working on vtd 1501\n",
      "looking for surrounds, now working on vtd 2001\n",
      "looking for surrounds, now working on vtd 2501\n",
      "On loop 2 for county 63  we found that vtd frag 2802 now has only 1 neighbor 2845\n",
      "On loop 2 for county 32  we found that vtd frag 2932 now has only 1 neighbor 2960\n",
      "After surround checks, we appear to have 2558 building blocks defined. We captured 281 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "WARNING - unit 785  = vtd 862 in county 8 has only 1 neighbor= [786] . Optional triage in next block\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "working on unit neighbor lists for county 80\n",
      "WARNING - unit 249  = vtd 2705 in county 87 has only 1 neighbor= [248] . Optional triage in next block\n",
      "working on unit neighbor lists for county 100\n",
      "working on unit neighbor lists for county 120\n",
      "working on unit neighbor lists for county 140\n",
      "All done creating unit lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ro0bFPvEKreNQSNsH3z9jNs7+5X0lRyJyNt1WEymWEvdzVJFU2H6OzsXtNgenXfEybPwAbnoTGneD8Oi8/S4IsFsbo4hY6+if8MplcPs35ueDSAVV3t/fGlutogoIgNrNYMAMOPw7fH63uT3+Acg6ARs+gBFfwSU9LA1TRCx0uk2i22VtHCIVjJKjis4eCLe+C39+B8m/werXwHCb+967HvpOMQef7DDUTKhExH/Y8mqPDSVHIt5QclQZVI+BTreby10fgn1rYf27sHs5fDvO3B7VDup1sCpCEbHC6Vvrbre1cYhUMEqOKpsajcwp5nL46f9g7ZsQWhOiYq2OTETKm2qORIpF91kqq+oN4doX4cY3IPMYfHYnZJ+yOioRKU+emiMlRyLeUHJU2XUYAldPgk2fwb6frY5GRMpTQN7NAdUciXhFyZE/aHGNOQ/QXVQRv2LL+4hXzZGIV5Qc+YOIBhBaA36bZ3UkIlKeAtTmSKQ4lBz5A0c16Px3+P0bqyMRkfJkU5sjkeJQcuQvTh6CkOpWRyEi5UkNskWKRcmRP3Dlwp/fQ+OuVkciIuVJj/KLFIuSI3+QtBLS9ud3FCki/kE1RyLFouTIL+SNr7ThQ31IivgTm818Yk01RyJeUXLkDxp3gw7DYN1sSFpldTQiUp5sdv1TJOIlJUf+wGYzH+d3hEP0pVZHIyLlKcCePxi1iFwUJUf+om5rcKbBylc0jIiIP7HZwZ1rdRQiFYqSI3/R5gboOgZ+fAE+GgSZx62OSETKQ4Buq4l4S8mRvwgIgN5PwYivIOU3mNkN9qy0OioRKWtqkC3iNSVH/qZxVxi1wmyD9NEQyMmyOiIRKUsBgao5EvGSkiN/lPybeVtN7RBEKj81yBbxmpIjf7TwMTiyDXIyYGpDWPaC1RGJSFnRo/wiXlNy5I+GzoVOd0C9DuBywur/QOYJq6MSkbIQYFebIxEvKTnyR/ViYcB0uPxecz3zOKx909KQRKSM2AJUcyTiJSVH/sydk7+cmw2GYV0sIlI2AtTPkYi3lBz5s9jB0Heyufzj87A9wdp4RKT02dQgW8RbSo78WVAoNOmRvx7osC4WESkbepRfxGtKjvxd7eaAzVyu0cTSUESkDKhBtojXlBz5u3VvAwbUbQs1lRyJVDpqkC3iNSVH/u70GGvuHHPctdl9Yf0ca2MSkdKjmiMRrwVaHYBY7M8fzPmR7fD9M+ZyerLZWDsoxLq4RKR0qBNIEa95VXM0c+ZMYmNjCQ8PJzw8nPj4eBYuXOjZbxgGkyZNIjo6mtDQUHr27MnmzZvPe8ycnByefvppmjZtSkhICB06dCAhofBTU5MmTcJmsxWaoqKivAldinLNFKhWD2pekr/txB54/0brYhKR0hOg5EjEW17VHDVo0ICpU6fSrFkzAObMmcPAgQPZsGEDbdu25fnnn2fatGm8++67tGjRgmeeeYbevXuzbds2qlWrds5jTpw4kQ8++IA333yTVq1a8e2333LjjTeycuVKLr30Uk+5tm3bsmTJEs+63W4vzvnKmRp0hke2gs0GB7eYw4qsnQ27l4MrB+xBVkcoIiUREKjbaiJe8qrmaMCAAfTv358WLVrQokULnn32WcLCwli9ejWGYTB9+nQmTJjATTfdRLt27ZgzZw6nTp3io48+KvKY77//PuPHj6d///5ccskljB49mr59+/LSSy8VKhcYGEhUVJRnqlOnTvHOWM5my3taLbINtC1QY6SO40QqPjXIFvFasdscuVwuPvnkEzIyMoiPj2fXrl2kpKTQp08fTxmHw0GPHj1YuXIl99577zmP43Q6CQkp3LYlNDSUFStWFNq2Y8cOoqOjcTgcxMXFMXnyZC655BLOx+l04nQ6PetpaWnenqb/yUoza40Atn4NsbdaG4+IlExAIPyxGN7oYdYEBwSBPdDcHhCUty0wf+5ZPr3PXkS508cJKuK1xX2PIAjQs0JiLa+To8TEROLj48nKyiIsLIz58+fTpk0bVq5cCUBkZGSh8pGRkezZs6fI4/Xt25dp06Zx5ZVX0rRpU7777ju+/PJLXK78/3Ti4uJ47733aNGiBQcPHuSZZ56hS5cubN68mVq1ahV57ClTpvDUU095e4r+7cj2/OXP7zJrldrfYl08IlIyXR6AbQlmTbA7B1x5c3euuezKhuyMvP255u30c5Vz5+Tvc7vyl8ui921bgPcJWEBgfi24zQbY8ufn2nb6OIWOFQT24DOSuaASlssre2bc9uD8/afjFp9hMwzvBtTKzs4mKSmJEydO8Nlnn/HWW2+xbNkyTpw4QdeuXTlw4AD16tXzlL/77rvZu3fvWY2sTzt8+DB33303//vf/7DZbDRt2pSrr76ad955h1OnTp3zNRkZGTRt2pTHHnuMRx55pMhYz1VzFBMTQ2pqKuHh4d6ctn9JOwCrZ8LK/4MbXoeOQ62OSER8ldtdIInKKZxgnZVYnS8BO/O1BRKws157rvfIBYy8MSIvNCf/GK7ss2NyZReOz/Oe2eZyabfhstmLTqIK1a4VSAQD7IUTxoLThcrXbAodBpfuOZSxtLQ0IiIiyu372+uao+DgYE+D7M6dO7N27VpmzJjB448/DkBKSkqh5OjQoUNn1SYVVKdOHb744guysrI4evQo0dHRjB07liZNiu6QsGrVqrRv354dO3acN1aHw4HDoSExvFalFmxbAA3jzUf6RUSKEhAAAQ7Ajz5r3e4CNXE5ZydxnvXscyRY50rIijjG6XJuV/77nV4vmDC6cyHnVOFE0VPeVTipPJF3J6dpLwhT292ilLifI8MwcDqdNGnShKioKBYvXux5yiw7O5tly5bx3HPPXfA4ISEh1K9fn5ycHD777DMGDRpUZFmn08nWrVvp3r17ScOXc1nzOhzfDYM/0L1/EZEzBQRAQDAQbHUk3vtpBix5CvDqppHf8So5Gj9+PP369SMmJob09HTmzp3L0qVLSUhIwGazMWbMGCZPnkzz5s1p3rw5kydPpkqVKgwbNsxzjBEjRlC/fn2mTJkCwJo1a9i/fz8dO3Zk//79TJo0CbfbzWOPPeZ5zaOPPsqAAQNo2LAhhw4d4plnniEtLY2RI0eW0o9BADi0FX6bBz+/BR2GQN3WVkckIiKl6ch2CIuEsLpWR+LTvEqODh48yPDhw0lOTiYiIoLY2FgSEhLo3bs3AI899hiZmZncd999HD9+nLi4OBYtWlSoj6OkpCQCCtRGZGVlMXHiRHbu3ElYWBj9+/fn/fffp3r16p4y+/btY+jQoRw5coQ6depwxRVXsHr1aho1alTC0xfArHb9aQZ89zSEVofYQdBrotVRiYhIaat5CWTMM9teqSF4kbxukF2RlXeDrgrBMOCL0fDrXIi/H656EgIrYFWxiIhc2I8vwvf/hgfWQ+1mVkdz0Xy+QbZUArtXwOHfod6lsH89/PpfGPgfuPRvVkcmIiJlKeZyc358V4VKjsqbkiN/43bDu9cW3tbxNnMSEZHKLSDva19tjs5LjyL5m4AA6PYw1G6Rv63fc7r3LCLiD6IvA2yw92erI/FpanPkzw5ugZnx0Oxq87H9oFCrIxIRkbL26l/MB3Ga/hVCa0CbgRDV3uqozqu8v79Vc+TP6raGfs/DnpXwsbpFEBHxC4PeN59aS1oD696B17vDL+9ZHZVPUZsjf2azQdy9kJIIfyyxOhoRESkPdVvB3z41l105sOBR+N8YqN8JIttaGpqvUM2Rv9vyFWz4AC4bYXUkIiJS3uxB0P9FCK8P/3vIHIRYlBz5pVwnfDTYvO/88XBo3A16jLU6KhERsYI9CG5917yLsPIVq6PxCbqt5o9OHYPtCfnryb9C1gmoUtOykERExELRl0JIdchKtToSn6DkyB8FVy28HtlOT6qJiPgbVy4seRL+/B4yjkDGIWh9vdVR+QQlR/7IUQ2uuA9Wv2au931WyZGIiL/5/t+weqbZ5rRKLfNx/kbxVkflE5Qc+SObDfpOhvRk2Dwfti2E+pdZHZWIiJSndW9Du5tgwHSrI/E5apDtr2w2uHYaYANHmNXRiIhIeWt7AyR+Av8dCjlZVkfjU5Qc+bN1b0OgAzoMszoSEREpb9fNgOtfgR2L4Y3usH0R+M+gGeel22r+KtcJP8+CDkMgrI7V0YiISHkLCDDbG9XrCAnj4KNbISIG2t4Il/Q0H9apFml1lJZQzZG/Wvl/cPIgXHG/1ZGIiIiV6sXC7V/D8PlQr4P5/fDBTfBSC1j2gtXRWULJkb/66f/M+etdYdnz1sYiIiLWW/g4/P514W2Zx6yJxWK6reaP/vwenGnmsivbHJ1ZRET8W5Va5rz7/4P6nc2HdRp2sTYmiyg58jcnkmD9u/nr174Ef7nLsnBERMRH1G0DSavMdket+lsdjaWUHPmT5N/gzV7gzjH/Qxj2MTTobHVUIiJitV3LYN1s6DkOLh1udTSWU3LkT756wEyMRnwFjbubTyqIiIic7ueoSi3ITofQGvn73G5IPwDZGVCruV98d9gMw386NUhLSyMiIoLU1FTCw8OtDqd8GQY8Vd1cDqoKD/4C1aIsDUlERHyEKwc+vQO2/s9cr9XcTJAyj5nNMVzZ5vb6neCOBAgMLtfwyvv7WzVH/sLtAmyAATkZ8Mt70OMxq6MSERFfYA+CwR9A6n7YvRz2rTVrk0KrQ/WGUKMxfPp3OLABMg5DRH2rIy5TSo78hT0Quo2BFS+b68f3WBqOiIj4oIj6ZufAHYYU3n54G2SfhKufqvSJEaifI//S6fb85Zb9LAtDREQqkOxT8PFIqNUM/nKn1dGUCyVH/iR1X/7yvrXWxSEiIhXHgV/g8Fa4bjo4qlkdTbnQbTV/EnNF/nLqXti7FmL+Yl08IiLi+2o2BbsDfnzBbJx9MgU6DIXwaKsjKzOqOfIn9kC46ztzedNnsGiitfGIiIjvC68HQ/8Lx3bCl/fBd0/D0ilWR1WmlBz5m4K30+p3si4OERGpOJpdBde9nL/+y3vwy/vWxVPGdFvN36TtN+ehNaBRfP52twsC7NbEJCIivq9OS6jRxPyuqFoXvh4DIRHQ5npIO2AuB1e1OspSoU4g/c0fS+CDm/PXL78XfpsLOZlw4+vQ7uaiXysiIgLmd8a84fDnd9BzPPzwDDSMh78nlMnblff3t1e31WbOnElsbCzh4eGEh4cTHx/PwoULPfsNw2DSpElER0cTGhpKz5492bx583mPmZOTw9NPP03Tpk0JCQmhQ4cOJCSc/cN97bXXaNKkCSEhIXTq1Inly5d7E7qc1uxqeOIoNOttrm/4AGIHQ5XasOwFa2MTEZGKISgUrvwnGG4zMapkvEqOGjRowNSpU1m3bh3r1q2jV69eDBw40JMAPf/880ybNo1XX32VtWvXEhUVRe/evUlPTy/ymBMnTuSNN97glVdeYcuWLYwaNYobb7yRDRs2eMrMmzePMWPGMGHCBDZs2ED37t3p168fSUlJxTxtP2cPNAedHbcPxu+H/i9A9RjIPA5bvoLcbLOK1H8qFUVExFsxl0OfZ+HaadCkBwRUnpY6Jb6tVrNmTV544QX+/ve/Ex0dzZgxY3j88ccBcDqdREZG8txzz3Hvvfee8/XR0dFMmDCB+++/37PthhtuICwsjA8++ACAuLg4LrvsMmbOnOkp07p1a2644QamTCm6xbzT6cTpdHrW09LSiImJ8e/bakXZuRT+9xAc352/rW5biLsXLv2b2iOJiMi5HdkBr10B3R+Fv44rk7fw6dtqBblcLubOnUtGRgbx8fHs2rWLlJQU+vTp4ynjcDjo0aMHK1euLPI4TqeTkJCQQttCQ0NZsWIFANnZ2axfv77QcQH69Olz3uMCTJkyhYiICM8UExPj7Wn6j0t6wuAP89f7vwg1GsH/HoQZHeD17pCyybLwRETER333FFSLhm4PWx1JqfE6OUpMTCQsLAyHw8GoUaOYP38+bdq0ISUlBYDIyMhC5SMjIz37zqVv375MmzaNHTt24Ha7Wbx4MV9++SXJyckAHDlyBJfL5fVxAcaNG0dqaqpn2rt3r7en61+i2sGdi2HUT3D53Wa/FmGRZoeRKb/BwfO3HxMRET+Tsgm2/g96Pg5BIRcuX0F4nRy1bNmSjRs3snr1akaPHs3IkSPZsmWLZ7/NZitU3jCMs7YVNGPGDJo3b06rVq0IDg7mgQce4I477sBuL3wbx9vjgllzdbrx+OlJLiDmcjNJOq1pr/zl1a+ZY+yIiIiAOZh59Ubmgz2ViNfJUXBwMM2aNaNz585MmTKFDh06MGPGDKKiogDOqs05dOjQWbU+BdWpU4cvvviCjIwM9uzZw++//05YWBhNmjQBoHbt2tjtdq+PK6Vk4Gv5y8kbYdlU717vdpdqOCIi4kNys6BqHbAHWR1JqSpxD9mGYeB0OmnSpAlRUVEsXrzYsy87O5tly5bRpUuXCx4nJCSE+vXrk5uby2effcbAgQMBMxnr1KlToeMCLF68+KKOKyVksxX+j+CnGZCWfOHX7f4JXm4PT9eAT/+uJ99ERCqjtjfC/nVwaKvVkZQqr567Gz9+PP369SMmJob09HTmzp3L0qVLSUhIwGazMWbMGCZPnkzz5s1p3rw5kydPpkqVKgwbNsxzjBEjRlC/fn3PU2Zr1qxh//79dOzYkf379zNp0iTcbjePPfaY5zWPPPIIw4cPp3PnzsTHxzNr1iySkpIYNWpUKf0YpEg2G9w0CzKOmJ19XX4PhNWFXCcc32OOuXPmKM3Hdpnj7qTmdbWw6TPo8g+zP4y6bSvVfWkREb/W5Epznvwb1G1tbSylyKvk6ODBgwwfPpzk5GQiIiKIjY0lISGB3r3NDgUfe+wxMjMzue+++zh+/DhxcXEsWrSIatXyvzyTkpIICMivsMrKymLixIns3LmTsLAw+vfvz/vvv0/16tU9ZQYPHszRo0d5+umnSU5Opl27dixYsIBGjRqV8PTlotXvZCZHP8+C9e+CLcCsTnWEQ7/noOMwSD8I3z8NGz4EzqgpmtXTnHd/FK56opyDFxGRMlG1jvlP74b3IXaQ+Q91JaDhQ+TiHdhojsqccRiyMyD6Uvh1rjn8SIeh4MqBTZ+aZe9ZZvZ9EVoD6l8G2xaaozk37QW9/1240beIiFRMJ5Lg+2fgt3kw/Ato+tcyeZvy/v6uPN1ZStmL7mhOBTXpYSY/S6fiqS1qcPnZZS+9zRz0dtWr8HpX83XD5pld0IuISMWTeRymtzeXHRFQLcraeEqRkiMpmYAAsxftRl1g0UQ4dQx6P3XuspffY/a0/ecPsGsZLH8Jek0s33hFRKTkNv4Xvshr9zt8PjTpaX4fVBJKjqR0RLWHEV+ev8yq/8CPz+ev//iC2Vj7xjfM/pVERKRiOJTXv2FQFXDlVqrECJQcSXlq0ddso2QPguoNIaK+2Y5pdm+45R1odS0EOsyyx3aZt+EaxoPbZTb2yz4Jdoc51pvNZrZ7stlh94+QngIRMWaSVr3hxTUKdOWaT9AFBpfpaYuIVDq9JpoP5fw8C36ZAy36XPg1FYiSIyk/DTrDw4mFt+1ZCe/0g0/vAGzQZqCZ4PwwGQwXRLY3uw7487v81/w0w2wU7s7J32Z3gCtvkGFHhHmMDoOh49/O/o9m7WxY+5b5n4/Nbj5p1+NxqK6x90RELkqgI79vozYDrY2lDOhpNbGW2w17foKsE5C6H5Y9B5nH4LKR0OZ6WDwJDhZIqK5+ynwirmV/qNcBcjLN9k7V6pm1RymJ5jhwe3+GHd+aT8fdPBuq1DRfv389vNkLml0NrQeYbaRW/cd8/24Pqw2UiMjF+mGy+Znd5gYYNKdM36q8v7+VHIlvyTwOWalQo7G57kyHD26BvauhUTcY8oHZPcDF+PN7+PROs+bp7h/MKuC3rjY7rbxzcf7tNOdJmFIfAoJgbBIEVymTUxMRqVTmj4Jf/2v2e9d1DFz5zzL7/FRyVIaUHPmhpNXwdl9o3sdsx5R5zEyMajUtXG5qI7P2qN0t8Jc7zdooEREpmtsFW/8Hn4w014d9bLYtLQPl/f1duZqXi5wp+jKIiYN966B2Cxj59dmJEZhVwi36mbfs3rsBstLKPVQRkQolwA6bPzeXL78XLulpaTilSTVHIgWtfh0SHoe2N8Gt71gdjYiI78rJhFc6QUQDuHNRmb6Vao5ErHTFKOj/ovnf0K7lVkcjIuK70g6YXa7sW2c+3FKJKDkSOVPnO6FeR5hzHbw3ELZ8ZfaJJCIi+bIzzLnhgvRka2MpZUqORM4UEAB/T4AbXofsU/DxcJj3N8jJsjoyERHfcbqLlLBIiGxrbSylTMmRyLkEhULHoXDXYhjyX9j5A0xrZfaJZBiqSRIR/5a6D17vbi6fPJhfi1RJKDkSuZBW/eHe5VCnNXw7Hp6/BP5dCzZ8aHVkIiLWCK5qPsp/2qrXrIulDCg5ErkYdVpA/+fNXrkz8xoe7vrR2phERKwSWgN6T8pfb3+LZaGUBSVHIhcrqj3ckZC/HhRiXSwiIlZrPwgCQ83esWs2sTqaUqXkSMQby1/KX/7lPeviEBGxWvJGyM2Ext2tjqTUBVodgEiFUqOROW96FXS+w9pYRESslJb3+H7V2tbGUQZUcyTijctGQI0mkHEYHOHmk2siIv7m+B5Y9pzZDjOqvdXRlDolRyLeGjDDTIreux5m94Ejf1gdkYhI+TAMWDsb3rgSXE646S1zjLVKRsmRiLcu6QGjlsOwT+D4Lvj+31ZHJCJSPnYuhW8eMQfyvvdH80neSkjJkUhx2GzQ5EoIDIEqtayORkSkfITXh+AwOPw72IOtjqbMKDkSKa5Nn0LqXggJN4cZERGp7Go1M4cNcaYpORKRc2jZH1pcAytehheawQc3q/2RiFRuNhtc8ldz+YvR4HZbG08ZUXIkUlxVasKwefDgBrjyUdi5DNa/Y3VUIiJlx2aD6/8PbnkbEj+BdbOtjqhMKDkSKamal0D3R8xkad86q6MRESl7pzt+dKZbG0cZUXIkUlrCIsGVbSZIW74sPCijiEhlsmOROW99vbVxlBElRyKlpVFXOLgJ3roKPh4BP0y2OiIRkbLx2zxo0gNqN7M6kjKh4UNESkuPx+DYn4ANAh2w8hWIbAvtbrI6MhGR0pOeAruWm22PKimvao5mzpxJbGws4eHhhIeHEx8fz8KFCz37DcNg0qRJREdHExoaSs+ePdm8efMFjzt9+nRatmxJaGgoMTExPPzww2RlZXn2T5o0CZvNVmiKioryJnSRslelJtz2Cdz2MQx81bzFpgbaIlLZJK0CDGje1+pIyoxXyVGDBg2YOnUq69atY926dfTq1YuBAwd6EqDnn3+eadOm8eqrr7J27VqioqLo3bs36elFN9j68MMPGTt2LE8++SRbt25l9uzZzJs3j3HjxhUq17ZtW5KTkz1TYmJiMU5XpJysfQswoOPfrI5ERKR0hdY05z++AAc2VsoxJr26rTZgwIBC688++ywzZ85k9erVtGnThunTpzNhwgRuusm8jTBnzhwiIyP56KOPuPfee895zFWrVtG1a1eGDRsGQOPGjRk6dCg///xz4UADA72uLXI6nTidTs96WlqaV68XKZbNX8B3T0OPsRA7yOpoRERKV+Nu0OUfsP49WPsmRMRA8z5Quzl0vM3sGLeCK3abI5fLxSeffEJGRgbx8fHs2rWLlJQU+vTp4ynjcDjo0aMHK1euLDI56tatGx988AE///wzl19+OTt37mTBggWMHDmyULkdO3YQHR2Nw+EgLi6OyZMnc8kll5w3xilTpvDUU08V9xRFiuen6dCoG/Qca/YJIiKV2/5fIOsEGG6zU0Sj4OTKXz7vPpdZA3Pm9vPtcxcsZ5xnn/scx7vI7W4XuHPNY7tzzXM4vR4aAc5Uc6SA0/0dHdpaKdoieZ0cJSYmEh8fT1ZWFmFhYcyfP582bdqwcuVKACIjIwuVj4yMZM+ePUUeb8iQIRw+fJhu3bphGAa5ubmMHj2asWPHesrExcXx3nvv0aJFCw4ePMgzzzxDly5d2Lx5M7VqFT2u1bhx43jkkUc862lpacTExHh7yiLeqdUc9qyEzONmOyQRqbyO/glv/rVkx7DZwRZgTgEFlm228+wLKGK7Pe91F9h31nY7BAaevS8g0NwXkDfZ7Oa2c60DNOtd8p+pD/A6OWrZsiUbN27kxIkTfPbZZ4wcOZJly5Z59tvO+E/ZMIyzthW0dOlSnn32WV577TXi4uL4448/eOihh6hXrx5PPPEEAP369fOUb9++PfHx8TRt2pQ5c+YUSn7O5HA4cDgc3p6iiPd2LDZHq24YD/YgSNsHq1+DXhOtjkxEylJOpjkf8hHU63COZMZ2RhJyZtKi2mVf5HVyFBwcTLNmZr8GnTt3Zu3atcyYMYPHH38cgJSUFOrVq+cpf+jQobNqkwp64oknGD58OHfddRdgJj8ZGRncc889TJgwgYCAs9uMV61alfbt27Njxw5vwxcpfYYBn/7dHIhx1avmB96lf4NOt1sdmYiUl2pRENHA6iiklJS4E0jDMHA6nTRp0oSoqCgWL17s2Zednc2yZcvo0qVLka8/derUWQmQ3W7HMAyMIlrAO51Otm7dWigJE7GMzQaNukB4fbhvDYw/AAP/ow9KEb9Q+Z7UEi+To/Hjx7N8+XJ2795NYmIiEyZMYOnSpdx2223YbDbGjBnD5MmTmT9/Pps2beL222+nSpUqnifRAEaMGFHoMf0BAwYwc+ZM5s6dy65du1i8eDFPPPEE119/PXa7eQ/z0UcfZdmyZezatYs1a9Zwyy23kJaWdlajbRHL9H/BbGP0y3sQFGp1NCIiUgJe3VY7ePAgw4cPJzk5mYiICGJjY0lISKB3b7MB1mOPPUZmZib33Xcfx48fJy4ujkWLFlGtWjXPMZKSkgrVFE2cOBGbzcbEiRPZv38/derUYcCAATz77LOeMvv27WPo0KEcOXKEOnXqcMUVV7B69WoaNWpU0vMXKZnM4/Dbx5CeDIEhsO/nC79GRCohtR2qTGxGUfeuKqG0tDQiIiJITU0lPLzi98MgFktLhnf7Q+o+CI+GkOrw1/HQovL2GisiZ0hJhNe7wd0/QP3LrI6m0irv72+NrSb+5eRh+HqM+XTZX+6Ea6acXebUMZg7DE7shUFzoEHnwvtzs2HbAnNg2Vwn3P8z1GxSLuGLiI/xn/oFv6LkSPxHrtNMeo7vgg6DzUftA0Og9XVgd4DLafbXcXCLOXaQ3QEr/w8GvZd/jKN/wn+HwpFtUL8zDH5fiZGI6JH8SkbJkfiPTZ/BvrVw1xKzNqh6Q/j+GVgx7dzlYwfBhvdh7m3Qcxyc2ANfPgBVa8OonyCqXfnGLyIi5ULJkfiP43sgtHr+bbIr/wkdhpqNqnOzITAYstLgjyVmX0U9HodmV8GCx+D1ruZrGnc3a4tCa1h2GiLiS3RbrTJSciSVn2HAD8/CiulmslNQRIOz+yNq3DV/ue2N0PQqOPALVKkNkW1VfS4iUskpOZLKb/078OMLZk1Qt4e9f31IOFzSs9TDEpHKRP80VSZKjqRyMwxY9za0us58zF5E5FxcOZCdkT+IqmdA1RIPJCEVkJIjqbwyjsDyl8x+SHr/2+poRMSXzRlgPqV6LoVGpi8wcn1AIBhus4xut1cqSo6kbLlyITcLXNnmf2ZV65T9f2LpKWYbow0fmB9ifSdD07+W7XuKSMWWug9aD4A2N5gJjzsX3C5zbrjA7S6wfHq721wOCoW6baw+AylFSo6kdG392nz8/cBGyDiU/1/VaY5wiGxn9ijd8Arz6a86LS/uv66cLEjda74Wm9mZ4/Zvzde2HwQN/gJJK+HXuRDoMGuLYgdBWN0yOFERqVTcueZnU/tbrI5EfICSIyk9q16Db8dBg8vhsuFQLQoCQ81EJdBh1uIc/h0ObYUTSZDwJbjzapMadzMTpSZXQq1mZydLe3+GD2+FrBNgDzZfk3EYuv8/yMmEX+bAz29AtXrQ5UG4YpQetxeRi5frND9bRFByJKXp14/Mhs9DPiy6TKtr85ezM2DvGti1HHavgIWPmf+9hUWZyVKT7hATZ35gLXgUImLMPoZSNsHu5WYVeMdh5rGungTOdHBU071/EfFe9knz80MEJUdSWnKdZvsie9DFvya4KjTtZU4AzpOQtNpMfHYvh82f59+Wsztg+HyzD6ImV0L8fYWPZbOZj9yLiHgrJ69dpEOfIWJSciQl43bD2jfNDhZPHYH+LxT/WI4waH61OYHZW3VKovmhFdXeHLZDRKS0OdPNuWqOJI+SIym+E3vhi9FmLU/H26DrQ2bj6tISEl64t2oRkbLgTDPnSo4kj5IjKZ4di+HTv5sfJiO+VA/SIlJxqeZIzqDkSLy3/Vv471Bo3gdufN0czFVEpKJSciRnUHIk3jl5GD6/x0yMBn8Adv0KiUgF57mtpgbZYtI3W2VhGLB0Kqx53XzkvU4LwAYnD5qPqNbvZD763vjKi++hOvOE2fNroCN/26KJ5pNhA19VYiQilcPpmiM98Sp59O1WWfz+NSybCp3/bnZnf/QPs9PFqnWgeiPzVtjat8ynvq5/FaI7nv94Gz+CL/Iel293M9z4Bmz9En6bC9e/oifHRKTycKab/akV/EdQ/JqSo8rit4+hfme47uVz7zcM2LMSEh6Ht66GXhMh7t78Mc9ynWa/Q6HVzcfnv34E2lxvHnPJJHO8sv3rof2tcOnw8jwzEZGy5UxTeyMpRMlRZWG4z98w2mYzH4u/63v44Rkz4VnyZOEy9mCIvgwObIDazc3aoqBQqB4DP74I7W6C/i+qB2oRqVxO964vkkfJUWURYDdrgS4kMBh6P22OPH1kh7luDzZ7oD6YaA7N0fo6uGykmRgBtL3RnEREKiMlR3IGJUeVxcHN0KjLxZevf5k5FXS6Z2oREX+SlaYn1aSQi3xsSXxerhOyUs25iIhcPGe6kiMpRMlRZRE3CrZ+Da92hi1fWh2NiEjFodtqcgYlR5VFlwfgvtVQty18PAIWjjWfUBMRkfPT02pyBiVHlUmdFjD0v9DvBVgzExI/sToiERHfp5ojOYOSo8rGZoO4vOE9Vr9mdTQiIr5PNUdyBj2tVlnV6wArXzFvralfIhGpTAzDHAnAcJl9vJ1edp9r/Xzb3eZcT6vJGbxKjmbOnMnMmTPZvXs3AG3btuVf//oX/fr1A8AwDJ566ilmzZrF8ePHiYuL4z//+Q9t27Y973GnT5/OzJkzSUpKonbt2txyyy1MmTKFkJAQT5nXXnuNF154geTkZNq2bcv06dPp3r27l6frJ9wuc/iPdjcrMRKRymXBP+HnWaV/3PB6pX9MqbC8So4aNGjA1KlTadasGQBz5sxh4MCBbNiwgbZt2/L8888zbdo03n33XVq0aMEzzzxD79692bZtG9WqnbvK8sMPP2Ts2LG8/fbbdOnShe3bt3P77bcD8PLL5lAY8+bNY8yYMbz22mt07dqVN954g379+rFlyxYaNmxYgtOvpNKTIW0/tBlodSQiIqXHMGDvz1ClltmZrc1udoBrC8ib28+YX+T2QAfUaGL12YkvMUqoRo0axltvvWW43W4jKirKmDp1qmdfVlaWERERYbz++utFvv7+++83evXqVWjbI488YnTr1s2zfvnllxujRo0qVKZVq1bG2LFjvYo1NTXVAIzU1FSvXlfhJK0xjCfDDSNlk9WRiIiUni3/Mz/btvzP6kiknJX393exG2S7XC7mzp1LRkYG8fHx7Nq1i5SUFPr06eMp43A46NGjBytXrizyON26dWP9+vX8/PPPAOzcuZMFCxZw7bXXApCdnc369esLHRegT58+5z0ugNPpJC0trdDkF9L2m/PwaGvjEBEpLcd3w/x7oUU/aHWt1dFIJed1g+zExETi4+PJysoiLCyM+fPn06ZNG0+iEhkZWah8ZGQke/bsKfJ4Q4YM4fDhw3Tr1g3DMMjNzWX06NGMHTsWgCNHjuByuc553JSUlPPGOmXKFJ566ilvT7Hi27kUQmtASHWrIxERKbm0ZPjkdsAGN76utpRS5ryuOWrZsiUbN25k9erVjB49mpEjR7JlyxbPftsZv7SGYZy1raClS5fy7LPP8tprr/HLL7/w+eef8/XXX/Pvf/+7UDlvjwswbtw4UlNTPdPevXsv9jQrpvSD8O0EWP8u9BynDxARqfhO7IVZPeHkIRj5FYRWtzoi8QNe1xwFBwd7GmR37tyZtWvXMmPGDB5//HEAUlJSqFcvv9X/oUOHzqr1KeiJJ55g+PDh3HXXXQC0b9+ejIwM7rnnHiZMmEDt2rWx2+1n1RJd6Lhg3tZzOBzenmLFkuuEn/4Pdi+HpFUQEARX/Qsuv8fqyERESm7lK+a4kQ9thGpRVkcjfqLEnUAahoHT6aRJkyZERUWxePFiz77s7GyWLVtGly5FjxZ/6tQpAgIKh2G32zEMA8MwCA4OplOnToWOC7B48eLzHtdvfPkA/Pg8BIfB1U/BI1ug+/9TrZGIVGyuXPjiPvj5DbjqCSVGUq68qjkaP348/fr1IyYmhvT0dObOncvSpUtJSEjAZrMxZswYJk+eTPPmzWnevDmTJ0+mSpUqDBs2zHOMESNGUL9+faZMmQLAgAEDmDZtGpdeeilxcXH88ccfPPHEE1x//fXY7XYAHnnkEYYPH07nzp2Jj49n1qxZJCUlMWrUqFL8UVRAmcch8WO4ZipcMdrqaERESs/OpbDxQ4h/AOLvtzoa8TNeJUcHDx5k+PDhJCcnExERQWxsLAkJCfTu3RuAxx57jMzMTO677z5PJ5CLFi0q1MdRUlJSoZqiiRMnYrPZmDhxIvv376dOnToMGDCAZ5991lNm8ODBHD16lKeffprk5GTatWvHggULaNSoUUnPv2JzhENQFUjdZ3UkIiKlq0pNc956gLVxiF+yGYb/DN2elpZGREQEqamphIdXkq7il70APzwLQz7U460iUnl8929YMQ3G7gVHmNXRiMXK+/tbA89WdN0fgUZd4ftnrI5ERKR0rJgOy1+Ebg8rMRJLKDmq6HJOgTsXgkKtjkREpHTsWgYxV0CvJ6yORPyU14/yi8XcLtjyBfzxPRzdAcm/AjYYNs/qyERESkeV2pD1p566FcsoOapI9q6Frx6Aw79DVCzUbW0OLtvmBoiob3V0IiIlZxhwIsl82ETEIkqOKop96+H9G6BOK7j7e6jfyeqIRERKX3oy7F0NvZ+2OhLxY2pzVFEsfgKq1TO7z1diJCKVVbV60OBy2JZgdSTix5QcVRSBDjixx3yKIyvN6mhERMqGzQZ1WsDJ8w8sLlKWlBxVFLfOgUuHm0OFLJ1qdTQiImXn2C6o1czqKMSPKTmqKBzVoFV/CK4GrmyroxERKX2GAZu/gOTfrI5E/JySo4pi2XPwwc0Q2RaufNTqaERESt+qV+GTkRDdEW6YaXU04sf0tFpF8etciB0MN76hvj9EpPJxu2D7txASAcPngz3I6ojEj6nmqKKo0wp2/QhpB6yORESk9G35EnYvh5veVGIkllNyVFFc9zIEBMF7A+HkYaujEREpXRs/gpg4aNHX6khElBxVGOH1YMQX4EyDD2+B7AyrIxIRKT25WWYfRyI+QMlRRVKrKdz2KRz9Az6907xHLyJSGUS2g/3rzSfWRCym5KiiqRdr9nm0YxF8O8HqaERESkezqyF1LxzaYnUkIkqOKqTmV8PVk2DNTNizyupoRERKLvO4OU9JtDYOEZQcVUw5WbBrGdgdEFrD6mhEREpu65fmvGZTa+MQQclRxbT4X7D7Jxg2D+q2sjoaEZGSu2YqhFSHX+ZYHYmIkqMKx+2C7Qvh0tug6V+tjkZEpHRENICaTfJvr4lYSMmRrzAMcJ4Et7voMtkZ8PUYOLEXOgwtt9BERMpFSIRGABCfoOFDrHZsJ3z3b/hjidmHUWAIXPJXc5DZmpdA6n7YswIObIRDW80Pjuv/Dxp0tjpyEZHSVbctbP2f1VGIKDmylDMd5lwP2KDLP6BGE0jbD9sWwlcPAnn9fdRpbSZDl40we4+t3tDKqEVEykbjrrD6P3B8N9RobHU04seUHFlp8b/g1DG4bxXUaJS/vdsYyEqFk4egam09kSYi/qFRV7AFwLYEuGKU1dGIH1NyVB6yT5mDxu78AQICoc1As6v8dW9D/xcLJ0anhUSYk4iIvwitDq0HwA/PQqfbISjE6ojETyk5KksZR80q4p/fNNsTVW8E7lxY9ao5iGyjbtD5TqujFBHxHT3HwZYv4fevof0tVkcjfkrJUVk4vgfWvglr3zbX//J3uHQE1G5uJkef3WU+rnrTmxCgBwZFRDzqtjaHEln9mpIjsYySo9K0ewUsmggHNkBwNfOe+RX3Q9Va+WXsQTBInZyJiBQpJ0sDa4ullByVlt0r4L0boH4nuOVtaN4HHNWsjkpEpGJJ2WR2X3LzbKsjET+m5Kg0nDwM8/4GjbrA3z4za4dERMR7Kb+Z81bXWRuH+DWvGrzMnDmT2NhYwsPDCQ8PJz4+noULF3r2G4bBpEmTiI6OJjQ0lJ49e7J58+bzHrNnz57YbLazpmuvvdZTZtKkSWftj4qK8vJUy9Dx3WYboqueVGIkIlISmScgqIqeVBNLeZUcNWjQgKlTp7Ju3TrWrVtHr169GDhwoCcBev7555k2bRqvvvoqa9euJSoqit69e5Oenl7kMT///HOSk5M906ZNm7Db7dx6662FyrVt27ZQucTExGKcbhnZt9acnzpqbRwiIhVd+gFzAFoRC3mVHA0YMID+/fvTokULWrRowbPPPktYWBirV6/GMAymT5/OhAkTuOmmm2jXrh1z5szh1KlTfPTRR0Ues2bNmkRFRXmmxYsXU6VKlbOSo8DAwELl6tSpU7wzLgut+kNke/h4OOxda3U0IiIVlysHck6ZT/2KWKTYz5G7XC7mzp1LRkYG8fHx7Nq1i5SUFPr06eMp43A46NGjBytXrrzo486ePZshQ4ZQtWrVQtt37NhBdHQ0TZo0YciQIezcufOCx3I6naSlpRWaykSNxnDXEqjdAr57qmzeQ0TEH3R72OwM8v0bzMG2RSzgdXKUmJhIWFgYDoeDUaNGMX/+fNq0aUNKSgoAkZGRhcpHRkZ69l3Izz//zKZNm7jrrrsKbY+Li+O9997j22+/5c033yQlJYUuXbpw9Oj5b2NNmTKFiIgIzxQTE+PFmXopKAS6PgS7l5sDxIqIiPeqRcHVT5mDch/fbXU04qe8To5atmzJxo0bWb16NaNHj2bkyJFs2bLFs99msxUqbxjGWduKMnv2bNq1a8fll19eaHu/fv24+eabad++PVdffTXffPMNAHPmnL+/oHHjxpGamuqZ9u7de1FxFFvr6yEsEpY9V7bvIyJSWRkGrHgZ6rQyJxELeJ0cBQcH06xZMzp37syUKVPo0KEDM2bM8Dw9dmYt0aFDh86qTTqXU6dOMXfu3LNqjc6latWqtG/fnh07dpy3nMPh8DxZd3oqU4HB0OUfsHk+pPhQg3ERkYpi14+QvBH6vwABdqujET9V4rErDMPA6XTSpEkTT4Pq07Kzs1m2bBldunS54HE+/vhjnE4nf/vb3y5Y1ul0snXrVurVq1ei2MtE3CiIaAjLnrc6EhGRimftW2aNUePuVkcifsyr5Gj8+PEsX76c3bt3k5iYyIQJE1i6dCm33XYbNpuNMWPGMHnyZObPn8+mTZu4/fbbqVKlCsOGDfMcY8SIEYwbN+6sY8+ePZsbbriBWrVqnbXv0UcfZdmyZezatYs1a9Zwyy23kJaWxsiRI4txymXMHgQ9x8LWr2DlK2YVsYiIXFhaMvz+jTkg90U2xxApC171kH3w4EGGDx9OcnIyERERxMbGkpCQQO/evQF47LHHyMzM5L777uP48ePExcWxaNEiqlXLH0YjKSmJgDMGW92+fTsrVqxg0aJF53zfffv2MXToUI4cOUKdOnW44oorWL16NY0aNfL2fMtHx2FwZJs5ztqmz81Rppv31h+7iMj57PkJDJcGnBXL2QzDf6o20tLSiIiIIDU1tezbHwH8+YPZODtpldkVfv8XIDy67N9XRKQiWjMLEsbCv47qn0kppLy/v0vc5kjOo+lf4Y6FcOscsxftmV1h33qroxIR8U27f4TojkqMxHJKjsqazQZtb4D7VkPt5jBnAOy5+E4xRUT8h83sGTurjDrsFblISo7KS5WaMPwLqH8ZzL1NnZuJiJyp1XVw6gik7rM6EvFzSo7KU3AVGPQehETAR0Pg1DGrIxIR8R2r/wMN49X5o1hOyVF5q1IThs2Dkwfhg5vh5CGrIxIR8RE2s/lBgL6axFr6DbRCnZYwfD6c2AOvdIKf31R/SCIikW0h8TNIWm11JOLnlBxZJbojPLDO7M9jwaPw1T+UIImIf7tmKoTXM/9hFLGQkiMrVakJ171sThveh5TfrI5IRMQ6IeFgC4CgEKsjET+n5MgXdLzN/EA4sNHqSERErFWjMfz2iQbvFkspOfIFgQ6IaADHdlodiYiItbqOAZcTTiRZHYn4MSVHvqJqXbN/DxERf3VoK8zLq0mv19HqaMSPKTnyFe4cCAiyOgoREeukbILM4xB/P0TUtzoa8WNKjnxFTiYEqhGiiPixGo3MuSsX3C5rYxG/puTIF2SfgqN/mv0fiYj4q/qdoXYLWDMTXrkM9qyyOiLxU0qOfMGBX8BwmeOuiYj4q4AAuGsJ9H8R7MHwzjXw9cPqA07KXaDVAQiwcykEh0Fke6sjERGxxu6f4JORZhMDV7Y5Aax7G1pdC82utjY+8StKjqzmdpu9wba/VeMJiYj/OrIdMg5D38nmwyn205PDHIxWpBwpObKaMxWyTsAlPa2ORETEOlVqmvP2gyCsjrWxiN9TcmS17AxzvuVLOPYnGG7ofGf+B4WIiD+o08qcH9mm5Egsp+TIauH1IW60ObbazqWQmwVr34abZkGT7lZHJyJSPlw55lxdmogPUHJkNZsN+k01J4DU/fD5PTDnOrN6+bIREBMHgcHWxikiUpYyDpvzqrWtjUMEJUe+J6I+jPwKfpkDy6dB4sfgCIdmV5mNtlv0U8NtEal83LnmXCMFiA/Qt6wvCrBD57/DQ7/BPcsg/gFzUNq5w+D1bpD8m9URioiUrpAIc+5MszYOEZQc+baAAIjuCD0fh3t/hDsXmwMyvtkLfnwRcp1WRygiUjpOJ0dZqdbGIYKSo4ol5nK4+zu4YhT8MBnevwkyT1gdlYhIyTnCzbmSI/EBSo4qmkAH9HkGbv8GDm6Cj0eYHUmKiFRkwVXMec4pa+MQQclRxdUoHgbNgV3LIPETq6MRESmZ04/wq7mA+AAlRxXZJT2hYRclRyJS8dmDAZvZ15uIxZQcVXRtbzA7j8w8bnUkIiLFZ7OZtUc5So7EekqOKromPcCdA4e2Wh2JiEjJBDpUcyQ+wavkaObMmcTGxhIeHk54eDjx8fEsXLjQs98wDCZNmkR0dDShoaH07NmTzZs3n/eYPXv2xGaznTVde+21hcq99tprNGnShJCQEDp16sTy5cu9Cb1ycrth02fmss1ubSwiIiUVGKI2R+ITvEqOGjRowNSpU1m3bh3r1q2jV69eDBw40JMAPf/880ybNo1XX32VtWvXEhUVRe/evUlPTy/ymJ9//jnJycmeadOmTdjtdm699VZPmXnz5jFmzBgmTJjAhg0b6N69O/369SMpKamYp10JZByFOQPgx+eh53jzMX8RkYosKARyM62OQgSMEqpRo4bx1ltvGW6324iKijKmTp3q2ZeVlWVEREQYr7/++kUf7+WXXzaqVatmnDx50rPt8ssvN0aNGlWoXKtWrYyxY8d6FWtqaqoBGKmpqV69zif9/JZhPBluGD++ZBi5OVZHIyJScq9ebhgLvftcF/9Q3t/fxW5z5HK5mDt3LhkZGcTHx7Nr1y5SUlLo06ePp4zD4aBHjx6sXLnyoo87e/ZshgwZQtWqVQHIzs5m/fr1hY4L0KdPnwse1+l0kpaWVmiqNC7pCREN4bun4Jk68J8r4I8lVkclIlJ8anMkPsLr5CgxMZGwsDAcDgejRo1i/vz5tGnThpSUFAAiIyMLlY+MjPTsu5Cff/6ZTZs2cdddd3m2HTlyBJfLVazjTpkyhYiICM8UExNzUXFUCLWawkMbYeTXcO1LEFYHPrwVti284EtFRHySI1w9ZItP8Do5atmyJRs3bmT16tWMHj2akSNHsmXLFs9+m81WqLxhGGdtK8rs2bNp164dl19+dvuZ4hx33LhxpKameqa9e/deVBwVRoAdmnQ3B6kd/gU0vQoSxoIr1+rIRES8V6UWZByxOgoR75Oj4OBgmjVrRufOnZkyZQodOnRgxowZREVFAZxVm3Po0KGzan3O5dSpU8ydO7dQrRFA7dq1sdvtxTquw+HwPFl3eqq0Auxw9ZNwfA8seBRys/P3ZR6H7AzrYhMRuRhVa8Opo1ZHIVLyfo4Mw8DpdNKkSROioqJYvHixZ192djbLli2jS5cuFzzOxx9/jNPp5G9/+1uh7cHBwXTq1KnQcQEWL158Ucf1K1HtYcAM2PA+TGsNc2+DVy+H5xrDS60habXVEYqIFC2kugbTFp8Q6E3h8ePH069fP2JiYkhPT2fu3LksXbqUhIQEbDYbY8aMYfLkyTRv3pzmzZszefJkqlSpwrBhwzzHGDFiBPXr12fKlCmFjj179mxuuOEGatWqddb7PvLIIwwfPpzOnTsTHx/PrFmzSEpKYtSoUcU87Uqs00ho8BfY9Cns/Rnqd4IrH4UlT8Gvc6HhFVZHKCJSBAMC1DexWM+r5OjgwYMMHz6c5ORkIiIiiI2NJSEhgd69ewPw2GOPkZmZyX333cfx48eJi4tj0aJFVKtWzXOMpKQkAs745d++fTsrVqxg0aJF53zfwYMHc/ToUZ5++mmSk5Np164dCxYsoFGjRt6er3+IbAOR/yq8bfN8SEkEwzC76RcR8TXuXAjw6mtJpEzYDMMwrA6ivKSlpREREUFqamrlbn90LjsWw4e3mI23+06GoFCrIxIRKezbCbBjETyw1upIxMeU9/e36i/9RfPeee2RPoRP7yz58Vw55vAlIiKlRTVH4iP0W+hPOt0Oacmw6lX47RMIq2s+yXbyEJw8CNknwZVt3nqLaAAdb4PwemcfJ2kNfHQrYIPWA6DfcxBctbzPRkQqG3eu+eStiMWUHPmby4bD1q/g8wJdJtiDISwSHNXAHmRuS/wElk6FtjeYt+Ia5T0ZmJYM7/YHbNDlH7DmDUj5DYbOhfDo8j4bEalMVHMkPkK/hf4mogHctwqy0iDjMITWMKczG2lnpcKGD2DtbDNRCq8PddtA9RjzA6zLg2a/Su1ugo8Gw5tXwd8XQo3GlpyWiFQCLiVH4hvU5shfhYSbQ5BUqXnup9dCIiD+fvjHerNWKHYQ2AJg9wqo0wraDDTLRbWHu783R9P+5A7zlpyISHG4cyEgyOooRFRzJBdgs0HLfuZUlGpR0P9F+OAm2LcWYs4e/kVE5ILU5kh8hGqOpHQ0udK8Pbc9wepIRKSiUpsj8RFKjqTkDAN+/xpysuDYLqujEZGKSsmR+Aj9FkrJZZ2AT243lw31fSQixeR2KTkSn6CaIyk5w4BW15nLW77QwJEiUjxqcyQ+Qim6lNzqmeZtNYA+z5pPupVETiak7jdroWo0hsDgEocoIhWAOxcC1KGsWE/JkZRcp9th38+wcynkZhZ/YNvdP8Hyl8zjGC5zW63mcPObEH1pKQUrIj5LbY7ER+i3UEouoj4M/wJ+eBa+fwbqtoVW/c8ul+uEfevgwAZwpsElfzWHK/l1LiStguO7ICrWHI6kTkvzg3LJJPjgZnhwo9k3k4hUXmpzJD5Cv4VSOmw26DkeDv8O826Dhl2gdnOzw8jDv8PBTZCSCLlZEFQFAh2w7DnztTUaQ4t+cElPaNG3cM1T7ZbwymWw/EXo/bQVZyYi5cWdo+RIfIJ+C6X0BATALe/AL3PMW2O7lplDkNRpCZFtoe2N0LgbRLYDbGaHkYHBUK9j0bfiIupD90fhh2fMIU96P60aJJHKSg2yxUcoOZLSZQ+Cv9xlToZhNqou6sOuYdzFHfPKR6FKDVj8JOxeDvcsA0dY6cUsIr7BnZs/+LWIhfQov5Qdm610/gu02aBpL7NR9tE/4OiOkh9TRHyP2hyJj9Bvofi+RRNh1X8gtCZc9S+I6mB1RCJSFvS0mvgI/RaKb8s4CitfgZbXws1vgcsJyRvMvpDCoszG3Hb9GotUCmpzJD5C3yri20JrQKOusO0beL6J+bRbQSHVzTZJcaOVJIlUdKo5Eh+h30LxbQEBMHw+7F4Bh7ZAeH2o2QSCq0Haftj6FSx6Av5YAkPnQVCI1RGLSHHlZEL6QaujEFFyJBVAoAOaXWVOBdVuBpf0gNbXw4e3wLv94Yr7oMU1eppNpCLKOAzOVKujENHTalIJXNIDbv8GsMFnd8K0NrB0KpzYC2631dGJyMWq39m8VS5iMdUcSeUQcznc/R0c3w1rZsGKl2HpFLA7IKIBVI8xuwLoOc6siRIR3+NyQqBujYv1lBxJ5VKjMVwzGXr8E/asNGuPUveaSdOKl6FOa+gw2OooReRccp3650V8gpIjqZxCa0Crawtve6WTOaSJkiMR35SbpeRIfILaHIn/uGwkbPwQvnkUsk+du4wrF1a/DrP7wDNR8O51Zq+9IlL2cnVbTXyDao7Ef3T5BwSFmj1ub0+A9reag+FGNIAj2+HQVtjwPhzYCC37mW2Udi+HjCNQLdLq6EUqP9UciY9QciT+w2aDy++GJj1g1Suw7m1YMa3A/gCIag93LjL7VFr4ONTvBFXrWBeziD9RzZH4CCVH4n/qtIDrX4H+L0HSKjh1BGq3hNrNzf9as1LhravMbSP/Z3ZEKSJlyzBUcyQ+w6tP/ZkzZxIbG0t4eDjh4eHEx8ezcOFCz37DMJg0aRLR0dGEhobSs2dPNm/efMHjnjhxgvvvv5969eoREhJC69atWbBggWf/pEmTsNlshaaoqChvQhc5W2Cw2UdSu5shql3+h7IjHNoPgiPb4NO/w7711sYp4g9c2eZcNUfiA7xKjho0aMDUqVNZt24d69ato1evXgwcONCTAD3//PNMmzaNV199lbVr1xIVFUXv3r1JT08v8pjZ2dn07t2b3bt38+mnn7Jt2zbefPNN6tevX6hc27ZtSU5O9kyJiYnFOF2Ri2CzwY1vwA0z4fgemDMAjv5pdVQildvpcRNVcyQ+wKvbagMGDCi0/uyzzzJz5kxWr15NmzZtmD59OhMmTOCmm24CYM6cOURGRvLRRx9x7733nvOYb7/9NseOHWPlypUEBQUB0KhRo7MDDQxUbZGUn4AA6DgM2gyEl9vBxo/gqiesjkqk8spVzZH4jmI3pnC5XMydO5eMjAzi4+PZtWsXKSkp9OnTx1PG4XDQo0cPVq5cWeRxvvrqK+Lj47n//vuJjIykXbt2TJ48GZer8OPTO3bsIDo6miZNmjBkyBB27tx5wRidTidpaWmFJhGvBFeFeh3g8O9WRyJSuanmSHyI18lRYmIiYWFhOBwORo0axfz582nTpg0pKSkAREYWfuQ5MjLSs+9cdu7cyaefforL5WLBggVMnDiRl156iWeffdZTJi4ujvfee49vv/2WN998k5SUFLp06cLRo0fPG+uUKVOIiIjwTDExMd6ergiE14f0on+HS0vPnj0ZM2ZMmb+PiE/KdZpz1RyJD/D6abWWLVuyceNGTpw4wWeffcbIkSNZtmyZZ7/NZitU3jCMs7YV5Ha7qVu3LrNmzcJut9OpUycOHDjACy+8wL/+9S8A+vXr5ynfvn174uPjadq0KXPmzOGRRx4p8tjjxo0rtD8tLU0JknjPnWu2Qypjn3/+uefWsojfUc2R+BCvk6Pg4GCaNWsGQOfOnVm7di0zZszg8ccfByAlJYV69ep5yh86dOis2qSC6tWrR1BQEHa73bOtdevWpKSkkJ2dTXBw8FmvqVq1Ku3bt2fHjh3njdXhcOBw6A9NSigwGLIzyvxtatasWebvIeKzVHMkPqTEHbgYhoHT6aRJkyZERUWxePFiz77s7GyWLVtGly5dinx9165d+eOPP3C73Z5t27dvp169eudMjMBsS7R169ZCSZhImWnc3ewUMuNImb5Nwdtqr732Gs2bNyckJITIyEhuueUWTzmn08mDDz5I3bp1CQkJoVu3bqxdu7ZMYxMpc56aIyVHYj2vkqPx48ezfPlydu/eTWJiIhMmTGDp0qXcdttt2Gw2xowZw+TJk5k/fz6bNm3i9ttvp0qVKgwbNsxzjBEjRjBu3DjP+ujRozl69CgPPfQQ27dv55tvvmHy5Mncf//9njKPPvooy5YtY9euXaxZs4ZbbrmFtLQ0Ro4cWQo/ApEzHP0T1r8LSWvAeRJyMs3t5VB7BLBu3ToefPBBnn76aX7dtIW/TXqDP3KrM2LSK9w9ezk33X4fcz/+lCkzXmftuvU0a9aMvn37cuzYsXKJT6RM6Laa+BCvbqsdPHiQ4cOHk5ycTEREBLGxsSQkJNC7d28AHnvsMTIzM7nvvvs4fvw4cXFxLFq0iGrVqnmOkZSURECBHodjYmJYtGgRDz/8MLGxsdSvX5+HHnrIc5sOYN++fQwdOpQjR45Qp04drrjiClavXn3OR/5FSiRlE7zZC1zOwtsbdjHHYCsHSUlJVK1aleuuu45/fpZI/wOv81LHVcAnJP8RQsOPD3PbgC6kbFrMlD+Ocsl1DxL87SJmz57NP//5z3KJUaTUnb6tZldyJNazGYZhWB1EeUlLSyMiIoLU1FTCw8OtDkd80bq34euH4ZHf4fguSN0HYXXN5Cjw3Ld5iyvH5ea9VXtYujUZV1YaP7w6jpgWbek25H7eHz+Sk8cP07RRFI81T+K3Zn9nf2grGh74luff+h8//aMhHWpkUtXmZKs7hmvnuajVqDUrF35KkF3DnUgFtOlz+PQOGLsXQvT5LIWV9/e3xlYTKahpL3P4kLf7QvwD0LIfVC/9JxxTT+Vw39vfc0PKq7xrX47dZtDDmUGLw38ybvsyJo7I4YtdEfy2M4nxS11krfmK7YlPs2BVO3jrf3zScTZzAqri+nMpI/iGWNsaTh46ydSFv/PEdW1KPV6RMnf69nVQqLVxiKDkSKSwGo3hzsWwdDIkjIWF/zQHoG0zEC79G9Qo+a3cHJebh97/iccPPU5jewov5A5mjxHJLuMdcFfj3dyu5GInpvEhbm5ykINdr+HT6U+zdOkP3Ni3L8HBwfwl5BDDhg0j9VRnZv5wHd/P6MK/rjjM+nXLSL2qORGh6hJAKhhnGgSGgl2/u2I9JUciZ6rbCga9B5knYNcy2P4trHkDlr+YlyQNh6p1wBFm1jIFh5mNSC+iL6Rcl5vmExbyYtDrNA04wF32Z/jnnbdyaUx1ui5fSkzLtoS1+isHknZzqs2trDMcNNv2M263m5YtW1K1alVGjx7NP//5T2rWrEnDhg35/ZOXyHYH0KtDDFe7Z3HP6zGM6nc5PVrUISCg7PtnEikVWWm6nSY+Q8mRSFFCq5vJUJuB0P8Fc3y1Vf+BzfPPLhsQaCZJjmrmFBxmJk+ntwWHkRsUxotL9zMjKImB9pWMy7mTVc763Pv+esIcgWxLSedI8DGO101l4xcfc2zvs7iys6nToBF3/Ws6v2VU489fD3DNHQ9z4Pgpht32NzIyTtLx0sv48NMveX71FmYZ/+adE39n3gc9eTP8Juo0bEndag7CQ4LIzHFRJdjO4L80pE41NXoVH+NMM//ZEPEBapAt4g23C47tND/InSch+6Q5d6blL2efBGe6OZ2x7cSJY2QQwozcm/jY1RMo3ZqdmqQxInARI+yLqGk7yUkjhENGdTIIIZRsjhPGzMinePv+/qX6viIl9uX9cOh3uPs7qyMRH6QG2SK+LMAOtZsX++W7ko5z42vmQMwdY6qT4cwlw5nLSWcuGdkuXO6S/a9yjHCm597CG7nX8deAjdSzHSXSdoKqZNHTvpFmtgM0PTwKPrzijJqtqgVqu6oV2Je3HlzVXA6qCgF6Gk7KgG6riQ9RciRSji5tWIPdU6895z7DMHDmus1E6XTC5HQVWM7fdtKZQ0a2i1POXE46XZzKPvM1gSxwXlHo+Je4DvBI4Ce0jK5JzQAbnDwE2TvNzi0L1nhxvgTNdkYiFXbGLcTCtxLNpOrctxnN41Qtl3HrpAJwpkFIdaujEAGUHIn4DJvNRkiQnZAgO7XDSt4myDAMMnNcpGbmkJaZS3pWDiFBg2heP+J8L4KcU/nJ0pm3CguuF9p3Ek4ehKN/FNieYS5fMNk6V3IVfnbt1cWsq3fliisrDao3tDoKEUDJkUilZbPZqBIcSJXgQOqdJx8640X5NToUPWD0RXO7zWTLkzClF06cztEuy7Oeuu/stl25med/v4CgvKcIq11EQhV2Rq1WtbO36xZi+clKhZCL/UUVKVtKjkSk7AQE5CUrYVDtwsUvyJV74Rqts9bTzFqJtANnl3Hnnv/9gqoWuH1Y8HZi1fxbisHnKHNW2bx2WxfZ5YNfcqaBQ8mR+AYlRyJScdgDzS4WQquX/FiGYY7ndaEE6/Ty6dqu7AzIPA4n9uZtyzBrxLIzwJV9/ve02S8ikTpf0lWwXF7SZa8kH+NqkC0+pJL8VYmIeMlmg6AQc6pau3SOmZt9diJVqA1Wen5C5Wm7lVcmPeXs9lrZJ8Fwn/897Y6ia688TyIWqL0qVPYcyZkVTyTmZJmDPaufI/ERSo5EREpLYDAE1oQqNUvneIYBuVlnJ1KFkqjzJF2njp5dNifjwu97+naiI9xsBxSSN3eEF1g+c3veuiNv8ibBcqaZc9UciY9QciQi4qtsNnMg1qBQoE7pHNPtymskXzCROkctlzM9r73WCfOWV+ZxOL7bXM5KNfcVeRvRltdb/BlJU1GJVvYp82VqkC0+QsmRiIg/CbDnD3NT0kbyOVn5iVJWXiLlzEuestIKL2elQtp+OLQ17zV52wt29VCtXgkDEikdSo5ERKR4TrfZqlbMbh/c7vwnCg0DqseUbnwixaTkSERErBEQkHdrTW2NxLeohzMRERGRApQciYiIiBSg5EhERESkACVHIiIiIgUoORIREREpQMmRiIiISAFKjkREREQKUHIkIiIiUoCSIxEREZEClByJiIiIFKDkSERERKQAJUciIiIiBSg5EhERESkg0OoAypNhGACkpaVZHImIiIhcrNPf26e/x8uaXyVH6enpAMTExFgciYiIiHgrPT2diIiIMn8fm1FeaZgPcLvdHDhwgGrVqmGz2QrtS0tLIyYmhr179xIeHm5RhP5N18B6ugbW0zWwnq6B9c68BoZhkJ6eTnR0NAEBZd8iyK9qjgICAmjQoMF5y4SHh+uPwWK6BtbTNbCeroH1dA2sV/AalEeN0WlqkC0iIiJSgJIjERERkQKUHOVxOBw8+eSTOBwOq0PxW7oG1tM1sJ6ugfV0Daxn9TXwqwbZIiIiIheimiMRERGRApQciYiIiBSg5EhERESkACVHIiIiIgUoORIREREpwK+So+3btzNw4EBq165NeHg4Xbt25YcffvDsP3r0KNdccw3R0dE4HA5iYmJ44IEHLjhQbc+ePbHZbIWmIUOGlPXpVDhl9fM/zTAM+vXrh81m44svviijs6jYyuoa3HvvvTRt2pTQ0FDq1KnDwIED+f3338v6dCqksrgGx44d4x//+ActW7akSpUqNGzYkAcffJDU1NTyOKUKp6z+DmbNmkXPnj0JDw/HZrNx4sSJMj6TiqusroHT6eQf//gHtWvXpmrVqlx//fXs27fP6/j8Kjm69tpryc3N5fvvv2f9+vV07NiR6667jpSUFMAcXmTgwIF89dVXbN++nXfffZclS5YwatSoCx777rvvJjk52TO98cYbZX06FU5Z/vwBpk+fftaYeVJYWV2DTp068c4777B161a+/fZbDMOgT58+uFyu8jitCqUsrsGBAwc4cOAAL774IomJibz77rskJCRw5513ltdpVShl9Xdw6tQprrnmGsaPH18ep1GhldU1GDNmDPPnz2fu3LmsWLGCkydPct1113n/WWT4icOHDxuA8eOPP3q2paWlGYCxZMmSIl83Y8YMo0GDBuc9do8ePYyHHnqotEKtlMry528YhrFx40ajQYMGRnJysgEY8+fPL42wK5WyvgYF/frrrwZg/PHHH8WOtzIqz2vw8ccfG8HBwUZOTk6x462MyuMa/PDDDwZgHD9+vKThVkpldQ1OnDhhBAUFGXPnzvVs279/vxEQEGAkJCR4FaPf1BzVqlWL1q1b895775GRkUFubi5vvPEGkZGRdOrU6ZyvOXDgAJ9//jk9evS44PE//PBDateuTdu2bXn00UdJT08v7VOo0Mry53/q1CmGDh3Kq6++SlRUVFmEXymU9d/AaRkZGbzzzjs0adKEmJiY0gq/UiivawCQmppKeHg4gYF+Nb74BZXnNZBzK6trsH79enJycujTp49nW3R0NO3atWPlypXeBelVKlXB7du3z+jUqZNhs9kMu91uREdHGxs2bDir3JAhQ4zQ0FADMAYMGGBkZmae97izZs0yFi9ebCQmJhr//e9/jcaNGxtXX311GZ1FxVVWP/977rnHuPPOOz3rqOaoSGV1DQzDMP7zn/8YVatWNQCjVatWqjUqQlleg9OOHDliNGzY0JgwYUIpRl55lPU1UM3RhZXFNfjwww+N4ODgs7b37t3buOeee7yKr8InR08++aQBnHdau3at4Xa7jeuvv97o16+fsWLFCmP9+vXG6NGjjfr16xsHDhwodMzk5GRj69atxhdffGG0adPGGD16tFcxrVu3zgCM9evXl+ap+iSrf/5ffvml0axZMyM9Pd2zzd+SI6uvwWknTpwwtm/fbixbtswYMGCAcdlll3n1hV6R+co1MAzDSE1NNeLi4oxrrrnGyM7OLovT9Um+dA38NTmy+hoUlRxdffXVxr333uvVuVT4sdWOHDnCkSNHzlumcePG/PTTT/Tp04fjx48THh7u2de8eXPuvPNOxo4de87Xrlixgu7du3PgwAHq1at3UTEZhoHD4eD9999n8ODBF38yFZDVP/8xY8bwf//3fwQE5N8hdrlcBAQE0L17d5YuXVq8E6tArL4G55KdnU2NGjV46623GDp06MWfTAXlK9cgPT2dvn37UqVKFb7++mtCQkKKd0IVkK9cA4ClS5fy17/+lePHj1O9enWvz6WisvoafP/991x11VUcO3aMGjVqeLZ36NCBG264gaeeeuqiz6XC34yuXbs2tWvXvmC5U6dOART6Ej297na7i3zd6dzR6XRedEybN28mJyfnor9IKjKrf/5jx47lrrvuKrStffv2vPzyywwYMOCCcVUGVl+D873O29dUVL5wDdLS0ujbty8Oh4OvvvrKrxIj8I1r4O+svgadOnUiKCiIxYsXM2jQIACSk5PZtGkTzz///EWdQ8E38wuHDx82atWqZdx0003Gxo0bjW3bthmPPvqoERQUZGzcuNEwDMP45ptvjLfffttITEw0du3aZXzzzTdG27Ztja5du3qOs2/fPqNly5bGmjVrDMMwjD/++MN46qmnjLVr13pe06pVK+PSSy81cnNzLTlXX1RWP/9zwc9uq12ssroGf/75pzF58mRj3bp1xp49e4yVK1caAwcONGrWrGkcPHjQknP1VWV1DdLS0oy4uDijffv2xh9//GEkJyd7Jn0OFVaWn0XJycnGhg0bjDfffNPzNNaGDRuMo0ePlvt5+rKyvAajRo0yGjRoYCxZssT45ZdfjF69ehkdOnTw+u/Ab5IjwzCMtWvXGn369DFq1qxpVKtWzbjiiiuMBQsWePZ///33Rnx8vBEREWGEhIQYzZs3Nx5//PFC94137dplAMYPP/xgGIZhJCUlGVdeeaVRs2ZNIzg42GjatKnx4IMP6o/hHMri538uSo6KVhbXYP/+/Ua/fv2MunXrGkFBQUaDBg2MYcOGGb///ns5n13FUBbX4HQbl3NNu3btKt8TrADK6rOoqDY377zzTvmdXAVRVtcgMzPTeOCBB4yaNWsaoaGhxnXXXWckJSV5HV+Fb3MkIiIiUpr8pp8jERERkYuh5EhERESkACVHIiIiIgUoORIREREpQMmRiIiISAFKjkREREQKUHIkIiIiUoCSIxEREZEClByJiIiIFKDkSERERKQAJUciIiIiBfx/x1LzSKcNdHIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "            \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, the piece we selected is surrounded.  KISS - take on this neighbor's neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ed5ebb3-f0ab-42c1-b0f1-34ebf9124dc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the neighbors for unit 785 in county 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the neighbors for unit 249 in county 87\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "troubleUs = [785,249]\n",
    "for u in troubleUs:\n",
    "    plotPoly(unitGeom[u],1.5)\n",
    "    for c in range(nCounties):\n",
    "        if u in countyUnitList[c]:\n",
    "            print(\"Here are the neighbors for unit\",u,\"in county\",c)\n",
    "            plotPoly(countyGeom[c],0.1)\n",
    "            plotCenter(\"county\"+str(c),countyGeom[c])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        plotCenter(uu,unitGeom[uu])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAP.exterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAP.exterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")\n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 103\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotPoly(countyGeom[29],0.2)\n",
    "        plotPoly(countyGeom[55], 0.2)\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5acda29e-28a9-474b-9706-20a79121a07e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 1 - we already have an ensemble of HDpoly's and their weights\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"pigs\" == \"can fly\": #countyNo in shapeDF.columns.values:\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "if len(unassignedList) > 0:\n",
    "    print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "    for t in unassignedList:\n",
    "        print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "        plotPoly(hdCP[t].buffer(0.1))\n",
    "        plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "        plotPoly(countyGeom[HDcountyNo[t]])\n",
    "        plotPoly(MAP,0.2)\n",
    "        plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2fb6d6e3-6157-4728-a6e5-ea22ede3de03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIONAL - write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"10Mar24\"\n",
    "print(\"OPTIONAL - write the unit topology to a file\")   #New for FL 2/17/24 - include parent VTDs\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c47fa1ff-2bed-4641-96e9-8dbdb7cb6da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cluster no 0 containing counties [22, 145, 40, 26]\n",
      "halfway there! last use was 0.92064\n",
      "our final fractional capture area to normalize this cluster's usage is 0.31094 from 201 intersecting HDs\n",
      "working on cluster no 1 containing counties [118, 67, 138, 126]\n",
      "halfway there! last use was 0.97687\n",
      "our final fractional capture area to normalize this cluster's usage is 0.34958 from 199 intersecting HDs\n",
      "working on cluster no 2 containing counties [24]\n",
      "halfway there! last use was 0.94448\n",
      "our final fractional capture area to normalize this cluster's usage is 0.30035 from 236 intersecting HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing w/option 1 - set the cluster area thresholds to unitize cluster use\n",
    "#NOTE -- THIS MAY NOT BE RIGHT -- DEPENDS ON HOW WE GENERATED THE HDpoly's\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    nIntersections = 0\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t].intersection(MAP.buffer(1)),0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]),\"from\",nIntersections,\"intersecting HDs\" )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t].intersection(MAP.buffer(1)),1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aad5e7db-93db-43d8-8196-dd6dd02f3014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\n"
     ]
    }
   ],
   "source": [
    "#print(\"I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\")\n",
    "#CCBareaFrac = [0.17, 0.25, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "217527a2-0acf-43ec-9a25-09e0aecce76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\n",
      "We'll later add nearby or jettison faraway contiguous units until we approach the aDP\n",
      "  the HD pops and lists already appropriately include captured corner county clusters\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HDno 0\n",
      "working on HDno 500\n",
      "working on HDno 1001\n",
      "working on HDno 1503\n",
      "working on HDno 2004\n",
      "working on HDno 2519\n",
      "all done assigning units to HDs based on original HD polygons\n",
      "Here is a scatterplot of active HD pop excess vs relative to target 765136.2857142857\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, compute the current unit usage, plot its histogram weighted by unit pop\n",
      "unit avg and sd usage are 0.92895 0.12371\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity ...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.9750772691474474\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.97122828164681\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.9804057316398108\n"
     ]
    }
   ],
   "source": [
    "#continuing w/option 1 - now we determine original (nonsmoothed) vtd lists for the original HD polygons\n",
    "print(\"This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\")\n",
    "print(\"We'll later add nearby or jettison faraway contiguous units until we approach the aDP\")\n",
    "print(\"  the HD pops and lists already appropriately include captured corner county clusters\")\n",
    "#see above block for preliminaries\n",
    "nonUnitCounties = list (set([c for c in range(nCounties)]).difference(set(unitCounties)) )\n",
    "areaFrac = [0.5 for u in range(nUnits)]  #default; we will accept unit counties if we capture half their area\n",
    "alter = input(\"enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage\")\n",
    "if alter == 1:\n",
    "    for u in range(nUnits):\n",
    "        if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "            CCBno = int(allUnits[u] )\n",
    "            areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "            print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "        CCBno = int(allUnits[u] )\n",
    "        areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "        #print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "# for c in unitCounties:   #UNCOMMENT TO ENFORCE UNIT COUNTIES TO EQUAL USAGE\n",
    "#    areaFrac[allUnits.index(c+0.5)] = unitCareaFrac[ unitCounties.index(c) ]\n",
    "\n",
    "origHDpop, origHDlist = [0. for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "for jj,t in enumerate(popHDlist):\n",
    "    if jj%500 == 0:\n",
    "        print(\"working on HDno\",t)\n",
    "    hC = HDcountyNo[t]\n",
    "    if hC in allFusedCounties:  #using a swelling technique as original HDpoly's look skewy\n",
    "        origHDpop[t], origHDlist[t] = clusterSwell(hdCP[t], CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, aDP)\n",
    "    else:\n",
    "        if hC in unitCounties:\n",
    "            iCP, iClist = countyPop[hC], [allUnits.index(hC+0.5) ] #automatically include the home county if small\n",
    "        else: #must probe in-county list vs HDpoly intersxn\n",
    "            iCP, iClist =  getPolyPop(HDpoly[t], unitCP, unitPop, countyUnitList[hC])\n",
    "        nonCP, nonClist = getNonHCunits(HDpoly[t], hC, allUnits, unitCP, unitPop, allFusedCounties, areaFrac, countyGeom,\n",
    "                                        neighborCountyList, countyUnitList)  #this will miss county clusters; see below\n",
    "        origHDpop[t]  += iCP + nonCP\n",
    "        origHDlist[t] += iClist + nonClist\n",
    "        for j, geo in enumerate(CCBgeom):  #special for corner clusters -- intersection area must clear threshold estbd in a prev block\n",
    "            unitNo = allUnits.index(j+0.25)\n",
    "            if geo.intersection(HDpoly[t]).area >= areaFrac[unitNo] * CCBarea[j]:\n",
    "                origHDpop[t] += unitPop[unitNo]\n",
    "                origHDlist[t].append(unitNo)\n",
    "print(\"all done assigning units to HDs based on original HD polygons\")\n",
    "HDpopExcess = list()\n",
    "for t in popHDlist:\n",
    "    HDpopExcess.append(origHDpop[t] - aDP)\n",
    "print(\"Here is a scatterplot of active HD pop excess vs relative to target\",aDP)\n",
    "plt.scatter(popHDlist, HDpopExcess)\n",
    "plt.axhline(-0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.axhline( 0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, compute the current unit usage, plot its histogram weighted by unit pop\")\n",
    "origUnitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in origHDlist[t]:\n",
    "        origUnitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(origUnitUse,bins=50,weights=unitPop)\n",
    "origUnitUseAvg, origUnitUseSD = getWeightedAvgAndSD(origUnitUse,unitPop)\n",
    "print(\"unit avg and sd usage are\",r5(origUnitUseAvg), r5(origUnitUseSD) )\n",
    "plt.show()\n",
    "print(\"prior to correcting for discontiguity ...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",origUnitUse[u])\n",
    "\n",
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fdec9f3-be08-4952-9745-bb0e47e5a04d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "599 416761.6348778783\n",
      "1296 242474.0\n",
      "1297 40300.0\n",
      "1299 203941.0\n",
      "1366 60994.0\n",
      "1375 73525.0\n",
      "1398 446938.0\n",
      "1421 444549.8830546292\n",
      "1426 427351.0\n"
     ]
    }
   ],
   "source": [
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 450000:\n",
    "        print(t,HDvPop[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6f443a63-ce36-4eba-b104-c84d3114c2de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73525.0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 1375\n",
    "print(HDvPop[t])\n",
    "for u in HDunitList[t]:\n",
    "    plotPoly(unitGeom[u])\n",
    "plotPoly(HDpoly[t].intersection(MAP.buffer(1)))\n",
    "plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70a8d159-68bb-4436-8a07-0ea73d3f5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#END OF OPTION 1 -- SKIP AHEAD TO AFTER OPTION 2\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e74fe279-1089-4774-8b1f-2bdc8d30887d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, we move in partial counties before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state FL\n",
      "clipPoly 0 intersects [43] counties and a total of 28 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"for some states like FL, we move in partial counties before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(countyGeom[43])\n",
    "        plotPoly(countyGeom[42])\n",
    "        plotPoly(cutLine.buffer(0.01))\n",
    "        plotPoly(altCutLine,0.2)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.008),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e7e21005-dc00-49b1-9719-62e526a12b2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-87.19171719578974 30.52313586297459)\n"
     ]
    }
   ],
   "source": [
    "print(tractCP[1111])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "73dbac1b-4bf3-480a-b028-6edf55a5447d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alternate simple approach for Florida Keys - translate all toward county CP\n",
      "I translated 28 vtds toward the center of county 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "5dc3a1ca-5aa2-47f9-9685-83a516dcd1fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "popHDlist = populatedTractList.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ecc512e1-cb48-4c0b-90f4-b90458ee23f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\n",
      "working on HD center 0\n",
      "working on HD center 500\n",
      "working on HD center 1001\n",
      "working on HD center 1502\n",
      "working on HD center 2003\n",
      "working on HD center 2513\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S  #For GA, we leave CCB's in the map, draw ALL vtds\n",
    "#Can draw for each vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "print(\"Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\")\n",
    "convexMAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    convexMAP = convexMAP.union(countyGeom[c])\n",
    "#convexMAP = MAP.centroid\n",
    "#for c in range(nCounties):\n",
    "#    if c not in allFusedCounties:\n",
    "#        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if tractPop[t] > 0:\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD center\",t)\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "650ae996-d3ca-422f-8ab0-3ffa3cf6eb67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54f09a52-c279-4647-9860-0729d1912b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\n",
      "working on unit-unit distances for unit 0 out of 2698 . Time = 0\n",
      "working on unit-unit distances for unit 400 out of 2698 . Time = 80\n",
      "working on unit-unit distances for unit 800 out of 2698 . Time = 147\n",
      "working on unit-unit distances for unit 1201 out of 2698 . Time = 202\n",
      "working on unit-unit distances for unit 1603 out of 2698 . Time = 245\n",
      "working on unit-unit distances for unit 2003 out of 2698 . Time = 275\n",
      "working on unit-unit distances for unit 2410 out of 2698 . Time = 292\n",
      "All done; total elapsed  296 seconds for GA with 14 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT; SEE LATER FOR unit - unit ...\n",
    "print(\"Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default = too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "#populatedTractList = popHDlist.copy()  #nomenclature\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc91a02a-52b2-4ac3-afc2-7f1650e32922",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 2698 . Time = 0\n",
      "working on unit-unit angles for unit 200 out of 2698 . Time = 22\n",
      "working on unit-unit angles for unit 400 out of 2698 . Time = 42\n",
      "working on unit-unit angles for unit 600 out of 2698 . Time = 61\n",
      "working on unit-unit angles for unit 800 out of 2698 . Time = 78\n",
      "working on unit-unit angles for unit 1001 out of 2698 . Time = 94\n",
      "working on unit-unit angles for unit 1201 out of 2698 . Time = 108\n",
      "working on unit-unit angles for unit 1401 out of 2698 . Time = 120\n",
      "working on unit-unit angles for unit 1603 out of 2698 . Time = 131\n",
      "working on unit-unit angles for unit 1803 out of 2698 . Time = 140\n",
      "working on unit-unit angles for unit 2003 out of 2698 . Time = 147\n",
      "working on unit-unit angles for unit 2205 out of 2698 . Time = 152\n",
      "working on unit-unit angles for unit 2410 out of 2698 . Time = 156\n",
      "working on unit-unit angles for unit 2613 out of 2698 . Time = 157\n",
      "All done with angles; total elapsed seconds = 158\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4e4690c9-a317-4944-b5fa-8ba6af7c26e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10711908.0 10711908 1.0\n"
     ]
    }
   ],
   "source": [
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / statePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3214fdc5-d104-4164-9478-7ab097ce01e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 3191.6299441340784 of targetWedgePop\n",
      "I am working on HD number 0 of 2698 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 2698 HDs.  Elapsed sec = 306\n",
      "I am working on HD number 800 of 2698 HDs.  Elapsed sec = 597\n",
      "I am working on HD number 1201 of 2698 HDs.  Elapsed sec = 870\n",
      "I am working on HD number 1603 of 2698 HDs.  Elapsed sec = 1205\n",
      "I am working on HD number 2003 of 2698 HDs.  Elapsed sec = 1446\n",
      "I am working on HD number 2410 of 2698 HDs.  Elapsed sec = 1670\n",
      "1885.346 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\n",
      "unit avg and sd usage are 1.00022 0.12219\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    neighborCountyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"unit avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aec0cf55-112b-4eba-93c6-9cf1d1b87aae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_10Mar.csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b7a38aff-e4c4-498a-9f63-b6d5d02ab158",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.average(HDweight) * nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "253554da-8842-470e-b9b3-5626823059b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\n",
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.62785\n",
      "our final fractional capture area to normalize this cluster's usage is 0.26146\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.79483\n",
      "our final fractional capture area to normalize this cluster's usage is 0.22671\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip this block, use one below if I already CREATED HDVTDLIST'S FROM POLYGONS and there are non-vtd units but all tracts are vtds\n",
    "print(\"Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\")\n",
    "#origHDlist = [list() for t in range(nHDs)],\n",
    "splitTractNo, splitTractUse = [-999]*nHDs, [0.]*nHDs\n",
    "origHDpop = [0. for t in range(nHDs)]  #these will be based on captured Census vtd's\n",
    "HDpoly, HDdiam = [dummyPoly for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "\n",
    "popHDlist = list()  #rebuild here again with the geometries\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001 :  #HD poly was actually drawn  #[(HDradius[t][w]+0.001*maxD) for w in range(4)]\n",
    "        HDpoly[t] = buildPoly(hdCP[t], HDradius[t], HDangle[t])  #expand to catch the units on HD edge\n",
    "        HDdiam[t] = HDpoly[t].area **0.5  #approximate, for later scoring purposes of underuse * distance\n",
    "        popHDlist.append(t)\n",
    "\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t],0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]) )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t],1.3)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "868ec654-07ad-4925-bea5-105e8fe9bc59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDtractList = [list(set(oldHDtractList[t] ) ) for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6f48a4-26fe-46a0-84e0-61cf3e042780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3/10/24 - Georgia -- not sure why unitUse is off here when I pull from original HDtractList.  Used polygons instead, but still off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63418340-35f5-4a32-a0aa-6b04478b3f57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no 1 containing counties [22, 145, 40, 26]\n",
      "halfway there! last use was 0.9714\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.17306 from 202 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 2 containing counties [118, 67, 138, 126]\n",
      "halfway there! last use was 0.98332\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.3148 from 203 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n",
      "working on cluster no 3 containing counties [24]\n",
      "halfway there! last use was 0.95399\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.30829 from 238 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 2 in or out\n"
     ]
    }
   ],
   "source": [
    "#new for FL - alt to above, using pop-capture threshold on vtd Lists, not area threshold on HDpoly's for including CCBs\n",
    "oldHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j+1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in oldHDtractList[t]:\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "beb8908f-1855-4224-9cbe-6f484a193f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping - is this from option 1??\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c1f835-ecad-444a-9f0e-0ab5c0e394e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4328e35e-089d-4f81-9987-a2cf0785a4ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\n",
      "Lets just pull in the HDtractLists from that HD2 code and use them\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2698 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "orig unit avg and sd usage are 0.94095 0.25629\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.6927301840156793\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.9326155527100091\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.7581629715265193\n"
     ]
    }
   ],
   "source": [
    "#ALTERNATE RESTART FROM A DECENT HDtractList - this may not be robust for states with county clusters\n",
    "print(\"Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\")\n",
    "print(\"Lets just pull in the HDtractLists from that HD2 code and use them\")\n",
    "\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDtractList\"] #HDvtdList\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "# HDvPop = inDF[\"HDvPop\"].to_list()  #don't care; will rebuild from units\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()        \n",
    "\n",
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])\n",
    "\n",
    "origHDlist   = [HDunitList[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "origHDpop =     HDvPop.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22916ca4-1217-4231-9f11-88110a1a7cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.9750772691474474\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.97122828164681\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.9804057316398108\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89cede45-92d7-4bc4-91e9-70b59c61a66b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no -1 containing counties [43]\n",
      "halfway there! last use was 0.86639\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.16293 from 255 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 0 containing counties [44]\n",
      "halfway there! last use was 0.90534\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.31677 from 262 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n"
     ]
    }
   ],
   "source": [
    "#skip for FL; already squared up\n",
    "oldHDtractList = [inVTDlist[t].copy() for t in range(nHDs)] #safekeeping\n",
    "HDtractList = [inVTDlist[t].copy() for t in range(nHDs)] #nomenclature\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j-1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in HDtractList[t]:  #HDtractList\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a85cac6-b8e8-41ea-a9f1-fcf68322c2dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, redo the unitlists and use after the CCB assignment changes\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00152 0.16838\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip for FL; already squared up\n",
    "print(\"OK, redo the unitlists and use after the CCB assignment changes\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in HDtractList[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in HDtractList[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in HDtractList[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            unitUse = [0. for u in range(nUnits)]\n",
    "            \n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b99795f-d030-456d-9201-6519b739b472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\n",
      "this optional RESTART block pulls in an existing vtdlist for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv ./2024state_HD_output/FL7211needsEnclaveRemoval.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00435 0.16718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\")\n",
    "print(\"this optional RESTART block pulls in an existing vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "\n",
    "#infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv\")\n",
    "#inDF = pd.read_csv(infile)\n",
    "#vtdListString = inDF[\"HDvtdList\"]\n",
    "#nHDs = len(vtdListString)\n",
    "#inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "#HDweight = inDF[\"HDweight\"]\n",
    "#HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "#hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "#hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "#print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 2679 populated HDs\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9175ad93-3ead-4f5a-8e58-1ae75653f8bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "714 219.78177952766418\n"
     ]
    }
   ],
   "source": [
    "print(t,time.time() - startTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1001 time is now 31\n",
      "working on HD 2004 time is now 57\n",
      "out of 2679 total HDs, there were 2419.0 contiguous and 2482.0 complement-contiguous HDs\n",
      "28 HDs had both discontiguity problems, while enclave-only = 169 and discontig only= 232\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 260 169\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE - continue here for option 1 or option 2 drawing method\n",
    "# 3/4/24 - for FL, using 6 or unspecified loops now yields same results\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,6) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ab9f20-ddb8-47b1-9e5d-5cfda17b2b25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 726879 district pop vs 765136 target\n",
      "we'll also trim any HD with total islands' pop <= 15302\n",
      "working on HD 1\n",
      "working on HD 2336\n",
      "Out of 260 discontig HDs 260 had discontig HDs.\n",
      "Of these, 234 won't be under 726879 after all minor discontigys were shed, while 26 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 1\n",
      "There are 26 HDs still with both discontinuity problems\n",
      "Additionally, 24 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 4\n",
      "Out of 193 HDpolys that generated enclaves, 0 had contiguous adjrs, while 113 neighbored a state boundary while 80 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 803393 district pop vs 765136 target\n",
      "working on enclave-y HD 117 . We have evaluated 0 of 193 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 431 . We have evaluated 100 of 193 enclavy HDs.Time is now 14\n",
      "Out of 193 HDs with enclaves 193 had enclaves.\n",
      "Of these, 171 wouldn't be over 803393 if all enclaves filled, while 22 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 113 28 6\n",
      "22 22 171\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92915 0.12312\n",
      "CCB cluster 0 = unit 2555 with pop 176742.0 now has use of 0.9750772691474474\n",
      "CCB cluster 1 = unit 2556 with pop 102191.0 now has use of 0.967817124642848\n",
      "CCB cluster 2 = unit 2557 with pop 295291.0 now has use of 0.9804057316398108\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7729308-3cd8-4c12-9199-f400f59e6f2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 22 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1604\n",
      "working on avg unit-to-HDcp dist for HD 2415\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([2555, 2556, 2557] )  #lock in the clusters\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce01cce3-e408-4cb1-8761-0cc0be4a57c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(allUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 22 HDs\n",
      "working on HD 444 cantFill 0 of 22 .Time is now 0\n",
      "working on HD 2583 cantFill 20 of 22 .Time is now 0\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.9291 0.12305\n",
      "CCB cluster 0 = unit 2555 with pop 176742.0 now has use of 0.9750772691474474\n",
      "CCB cluster 1 = unit 2556 with pop 102191.0 now has use of 0.967817124642848\n",
      "CCB cluster 2 = unit 2557 with pop 295291.0 now has use of 0.9804057316398108\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7a8176-52b6-4727-b1f4-679cdf3b7dfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "out of 2679 total HDs, there were 2652 23 0 4 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "\n",
    "#40 5 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74c4c39d-a238-4f10-81c4-949f67ea0d70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing HDdiam for HD 300\n",
      "computing HDdiam for HD 600\n",
      "computing HDdiam for HD 1200\n",
      "computing HDdiam for HD 1500\n",
      "computing HDdiam for HD 2100\n",
      "computing HDdiam for HD 2400\n",
      "computing HDdiam for HD 2700\n",
      "computing HDdiam for HD 3000\n",
      "computing HDdiam for HD 3300\n",
      "computing HDdiam for HD 3600\n",
      "computing HDdiam for HD 3900\n",
      "computing HDdiam for HD 4200\n",
      "computing HDdiam for HD 4500\n",
      "computing HDdiam for HD 4800\n",
      "computing HDdiam for HD 5100\n",
      "computing HDdiam for HD 5400\n",
      "computing HDdiam for HD 5700\n",
      "computing HDdiam for HD 6000\n",
      "computing HDdiam for HD 6300\n",
      "computing HDdiam for HD 6600\n",
      "computing HDdiam for HD 6900\n"
     ]
    }
   ],
   "source": [
    "#optional HDpoly building\n",
    "HDpoly = [hdCP[t] for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if t %300 == 0:\n",
    "        print(\"building HD shape from units for HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        HDpoly[t] = HDpoly[t].union(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD shape for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38257 but less than 191284\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 27 HDs to triage in this block\n",
      "working on swell-filling discontig HD 99 our 1 th HD we think we can swell out of 27\n",
      "we partially regrew 12 HDs, leaving 15 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList) )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.75 * aDP or contigPop > 2.*aDP - minPostFixPop:  \n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 0.9291 0.12305\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92903 0.12232\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1001 time is now 6\n",
      "working on HD 2004 time is now 12\n",
      "out of 2679 total HDs, there were 2664.0 contiguous and 2676.0 complement-contiguous HDs\n",
      "3 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 12\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 15 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 726879 district pop vs 765136 target\n",
      "we'll also trim any HD with total islands' pop <= 15302\n",
      "working on HD 916\n",
      "Out of 15 discontig HDs 15 had discontig HDs.\n",
      "Of these, 0 won't be under 726879 after all minor discontigys were shed, while 15 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 15 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 4 15\n",
      "{2690, 2627, 99, 740, 294, 1673, 617, 938, 940, 2687, 919, 799}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e16dfee-b5bc-4863-8bed-9a6b22290aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes about 3sec per HD :-( The total number of HDs to fix is 15\n",
      "swell-generating HD 916 our 1 th HD we must regrow out of 15 0 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes about 3sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%20 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 0.92903 0.12232\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92908 0.12051\n"
     ]
    },
    {
     "data": {
      "image/png": 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Op06cOKE1a9aopqZGkyZNUmlpqRITEyWdu1HcN+/JMnXq1K5/rqys1Pbt25WYmKhPP/1UkpSWlqYdO3boySef1FNPPaWxY8fK5XJpxowZl7FrAACgr/A7WCQpOztb2dnZ3X6vuLjYZ5tlWd8656JFi7Ro0aKeLAcAAPRxPEsIAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYLyIUC8AAC7VR3UtAZ9zyECb4gfbAz4vgMAiWAAYb8hAm+yR4cpxeQI+tz0yXHtXziZaAMMRLACMFz/Yrr0rZ+tka0dA5/2orkU5Lo9OtnYQLIDhCBYAvUL8YDtRAfRjXHQLAACMR7AAAADjESwAAMB4BAsAADAewQIAAIzHp4TQL1Q3tAX8I7FScG5kBgDwRbCgz6tuaNOctfvUdrozKPPbI8M1ZKAtKHMDAM4hWNDnnWztUNvpThU6p2jciEEBn59buwNA8BEs6DfGjRikSfGOUC8DANADXHQLAACMR7AAAADj9ShYioqKNGbMGEVHRys5OVkHDhy46Ph9+/YpOTlZ0dHRuv766/XSSy95fb+4uFhhYWE+r1OnTvVkeQAAoI/xO1hcLpdycnKUn58vt9ut9PR0zZ8/X1VVVd2OP3bsmBYsWKD09HS53W498cQTevTRR7Vz506vcTExMaqpqfF6RUdH92yvAABAn+L3Rbfr1q3T0qVLtWzZMklSYWGhdu/erc2bN6ugoMBn/EsvvaRrr71WhYWFkqSJEyfq0KFD+s1vfqP777+/a1xYWJhGjhzZw90AAAB9mV9nWDo6OlRZWamMjAyv7RkZGTp48GC376moqPAZP2/ePB06dEinT5/u2tbS0qLExESNHj1ad999t9xu90XX0t7erqamJq8XAADom/w6w1JfX6/Ozk7FxsZ6bY+NjVVtbW2376mtre12/JkzZ1RfX6+4uDhNmDBBxcXFuvnmm9XU1KT169dr1qxZOnz4sMaPH9/tvAUFBVq9erU/yweAbgXrjsXcowcInB7dhyUsLMzra8uyfLZ92/j/3j5z5kzNnDmz6/uzZs3StGnTtHHjRm3YsKHbOfPy8pSbm9v1dVNTkxISEvzbEQD92pCBNtkjw5Xj8gRlfntkuPaunE20AAHgV7AMGzZM4eHhPmdT6urqfM6inDdy5Mhux0dERGjo0KHdvmfAgAG69dZb9eGHH15wLVFRUYqKivJn+QDgJX6wXXtXzg7ac6ZyXB6dbO0gWIAA8CtYbDabkpOTVVZWpvvuu69re1lZmRYuXNjte1JTU/XHP/7Ra9uePXuUkpKiyMjIbt9jWZY8Ho9uvvlmf5YHAH6LH2wnKIBewO+PNefm5uq3v/2tXnnlFR09elQrVqxQVVWVsrKyJJ37Vc2SJUu6xmdlZemzzz5Tbm6ujh49qldeeUVbt27VY4891jVm9erV2r17tz755BN5PB4tXbpUHo+na04AANC/+X0Ni9Pp1IkTJ7RmzRrV1NRo0qRJKi0tVWJioiSppqbG654sY8aMUWlpqVasWKFNmzZp1KhR2rBhg9dHmhsaGvTII4+otrZWDodDU6dO1f79+zV9+vQA7CIAAOjtwqzzV8D2ck1NTXI4HGpsbFRMTEyolwOD/KO6UXdvLNeffvodHn6IK4Z/74BLc6k/v3mWEAAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgRoV4Aep/qhjadbO0I+LxDBtoUP9ge8HkBAL0fwQK/VDe0ac7afWo73Rnwue2R4dq7cjbRAgDwQbDALydbO9R2ulOFzikaN2JQwOb9qK5FOS6PTrZ2ECwAAB8EC3pk3IhBmhTvCPUyAAD9BBfdAgAA43GGBUb5qK6lV8wJALiyCBYYYchAm+yR4cpxeYIyvz0yXEMG2oIyNwAg+AgWGCF+sF17V84OyselJT4yDQC9HcECY8QPthMVAIBuESwAEETBuIaKM4bojwgWAAiCYF6XZY8M10uZyRoahOuyiCGYimABgCAI1nVZJ1o7lPU/lXrwlf8N6LznccdpmIpgAYAgCdZ1WcG6QJ07TsNkBAsA9DJcoI7+iDvdAgAA43GGpY+qbmgL2iljAACuNIKlD6puaNOctfvUdrozKPNz11gAwJVGsPRBJ1s71Ha6U4XOKRo3YlDA5+djjwCAK41g6cPGjRikSfGOUC8DAIDLxkW3AADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHjc6RYA4CUYDznlkR64XAQLAEDSuaiwR4Yrx+UJ+Nz2yHC9lJmsoUF4cCox1D8QLCFW3dCmk60dAZ0zGH87AtD3xQ+2a+/K2QH/f9KJ1g5l/U+lHnzlfwM673nEUP9AsIRQdUOb5qzdp7bTnQGf2x4ZriFB+I8XQN8WP9gelB/QwQgh6crE0N6Vs4kWAxAsIXSytUNtpztV6JyicSMGBXRu/lYAwCTBCiEpeDH0UV2LclwenWzt4P+nBiBYDDBuxCBNineEehkA0CsFM4ZgDj7WDAAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAONxa34AAC7io7qWgM/J8978R7AAANCNIQNtskeGK8flCfjcPAXafz0KlqKiIj3//POqqanRTTfdpMLCQqWnp19w/L59+5Sbm6sPPvhAo0aN0uOPP66srCyvMTt37tRTTz2ljz/+WGPHjtWvfvUr3XfffT1ZHgAAly1+sD0oT4LmKdA943ewuFwu5eTkqKioSLNmzdLLL7+s+fPn68iRI7r22mt9xh87dkwLFizQww8/rN/97nd67733lJ2dreHDh+v++++XJFVUVMjpdOoXv/iF7rvvPu3atUuLFy9WeXm5ZsyYcfl7CQBAD/AkaHOEWZZl+fOGGTNmaNq0adq8eXPXtokTJ+ree+9VQUGBz/if//znevvtt3X06NGubVlZWTp8+LAqKiokSU6nU01NTXrnnXe6xtx5550aMmSISkpKLmldTU1NcjgcamxsVExMjD+79K2qG9oCXtjSfyr7Tz/9jibFOwI+PwDAPP+obtTdG8tV6JyicSMGhXo5lyxY191c6s9vv86wdHR0qLKyUqtWrfLanpGRoYMHD3b7noqKCmVkZHhtmzdvnrZu3arTp08rMjJSFRUVWrFihc+YwsLCC66lvb1d7e3tXV83NjZKOrfjgfRFQ5u+92K5Tp0+G9B5z4uOHKCIzlNqagoLyvwAALNEdJ6S7ewpPbqt+5+bpoqOHKC3f/IdjQpwtJz/uf1t50/8Cpb6+np1dnYqNjbWa3tsbKxqa2u7fU9tbW2348+cOaP6+nrFxcVdcMyF5pSkgoICrV692md7QkLCpe6OMSY+H+oVAADw7YL586q5uVkOx4V/29Cji27DwrzPBliW5bPt28Z/c7u/c+bl5Sk3N7fr67Nnz+qrr77S0KFDL/q+UGhqalJCQoKOHz8e8F9X4fJxfMzFsTEbx8dcvenYWJal5uZmjRo16qLj/AqWYcOGKTw83OfMR11dnc8ZkvNGjhzZ7fiIiAgNHTr0omMuNKckRUVFKSoqymvb4MGDL3VXQiImJsb4f3H6M46PuTg2ZuP4mKu3HJuLnVk5z6873dpsNiUnJ6usrMxre1lZmdLS0rp9T2pqqs/4PXv2KCUlRZGRkRcdc6E5AQBA/+L3r4Ryc3OVmZmplJQUpaamasuWLaqqquq6r0peXp6qq6u1bds2Sec+EfTiiy8qNzdXDz/8sCoqKrR161avT/8sX75ct912m5577jktXLhQb731lvbu3avy8vIA7SYAAOjN/A4Wp9OpEydOaM2aNaqpqdGkSZNUWlqqxMRESVJNTY2qqqq6xo8ZM0alpaVasWKFNm3apFGjRmnDhg1d92CRpLS0NO3YsUNPPvmknnrqKY0dO1Yul6vP3IMlKipKzzzzjM+vsGAGjo+5ODZm4/iYqy8eG7/vwwIAAHCl8bRmAABgPIIFAAAYj2ABAADGI1gAAIDxCJYAKSoq0pgxYxQdHa3k5GQdOHDgouPb29uVn5+vxMRERUVFaezYsXrllVeu0Gr7H3+Pz2uvvabJkyfrqquuUlxcnH74wx/qxIkTV2i1/cf+/ft1zz33aNSoUQoLC9Mf/vCHb33Pvn37lJycrOjoaF1//fV66aWXgr/QfsjfY/Pmm29q7ty5Gj58uGJiYpSamqrdu3dfmcX2Qz35b+e89957TxEREZoyZUrQ1hcMBEsAuFwu5eTkKD8/X263W+np6Zo/f77Xx7u/afHixfrLX/6irVu36p///KdKSko0YcKEK7jq/sPf41NeXq4lS5Zo6dKl+uCDD/T666/rb3/7m5YtW3aFV973tba2avLkyXrxxRcvafyxY8e0YMECpaeny+1264knntCjjz6qnTt3Bnml/Y+/x2b//v2aO3euSktLVVlZqdtvv1333HOP3G53kFfaP/l7fM5rbGzUkiVLdMcddwRpZUFk4bJNnz7dysrK8to2YcIEa9WqVd2Of+eddyyHw2GdOHHiSiyv3/P3+Dz//PPW9ddf77Vtw4YN1ujRo4O2RliWJGvXrl0XHfP4449bEyZM8Nr2ox/9yJo5c2YQV4ZLOTbdSUpKslavXh34BcGLP8fH6XRaTz75pPXMM89YkydPDuq6Ao0zLJepo6NDlZWVysjI8NqekZGhgwe7f3T422+/rZSUFP36179WfHy8brjhBj322GNqa2u7EkvuV3pyfNLS0vT555+rtLRUlmXp3//+t9544w3dddddV2LJuIiKigqfYzlv3jwdOnRIp0+fDtGq0J2zZ8+qublZ11xzTaiXgv/36quv6uOPP9YzzzwT6qX0SI+e1oz/qK+vV2dnp8+DGmNjY30e6HjeJ598ovLyckVHR2vXrl2qr69Xdna2vvrqK65jCbCeHJ+0tDS99tprcjqdOnXqlM6cOaPvfe972rhx45VYMi6itra222N55swZ1dfXKy4uLkQrwzetXbtWra2tWrx4caiXAkkffvihVq1apQMHDigionf+6OcMS4CEhYV5fW1Zls+2886ePauwsDC99tprmj59uhYsWKB169apuLiYsyxB4s/xOXLkiB599FE9/fTTqqys1J///GcdO3as63lZCK3ujmV32xE6JSUlevbZZ+VyuTRixIhQL6ff6+zs1AMPPKDVq1frhhtuCPVyeqx3ZpZBhg0bpvDwcJ+/rdfV1fn8TfC8uLg4xcfHez1Oe+LEibIsS59//rnGjx8f1DX3Jz05PgUFBZo1a5Z+9rOfSZJuueUWDRw4UOnp6frlL3/J3+JDaOTIkd0ey4iICA0dOjREq8J/c7lcWrp0qV5//XXNmTMn1MuBpObmZh06dEhut1s/+clPJJ37i7NlWYqIiNCePXv03e9+N8Sr/HacYblMNptNycnJKisr89peVlamtLS0bt8za9YsffHFF2ppaena9q9//UsDBgzQ6NGjg7re/qYnx+frr7/WgAHe/2mEh4dL+s/f5hEaqampPsdyz549SklJUWRkZIhWhfNKSkr00EMPafv27VzzZZCYmBj9/e9/l8fj6XplZWXpxhtvlMfj6T0PGg7hBb99xo4dO6zIyEhr69at1pEjR6ycnBxr4MCB1qeffmpZlmWtWrXKyszM7Brf3NxsjR492lq0aJH1wQcfWPv27bPGjx9vLVu2LFS70Kf5e3xeffVVKyIiwioqKrI+/vhjq7y83EpJSbGmT58eql3os5qbmy2322253W5LkrVu3TrL7XZbn332mWVZvsfmk08+sa666iprxYoV1pEjR6ytW7dakZGR1htvvBGqXeiz/D0227dvtyIiIqxNmzZZNTU1Xa+GhoZQ7UKf5u/x+abe+CkhgiVANm3aZCUmJlo2m82aNm2atW/fvq7vPfjgg9bs2bO9xh89etSaM2eOZbfbrdGjR1u5ubnW119/fYVX3X/4e3w2bNhgJSUlWXa73YqLi7O+//3vW59//vkVXnXf99e//tWS5PN68MEHLcvq/ti8++671tSpUy2bzWZdd9111ubNm6/8wvsBf4/N7NmzLzoegdWT/3b+W28MljDL4hw3AAAwG9ewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjPd/8eGXx/cmqggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 0 time is now 0.0005426406860351562 sec\n",
      "working on HD 500 time is now 3.265219211578369 sec\n",
      "working on HD 1001 time is now 7.110544204711914 sec\n",
      "working on HD 1503 time is now 10.201613426208496 sec\n",
      "working on HD 2004 time is now 13.547917127609253 sec\n",
      "working on HD 2519 time is now 17.900555849075317 sec\n",
      "out of 2679 total HDs, there were 2679 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 18 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)\n",
    "#3-2-24 FL timing 5-> 214s, 368; 4 --> 130s, 4xx; 3-> 76s, 470; 6 -> 329s, 275 ; 2 with full check --> 810sec, only 2 discontig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "1.05707     0     8286 0.5\n",
      "1.0767     1     11140 0.5\n",
      "0.76768     2     2876 0.5\n",
      "0.89558     3     12583 0.5\n",
      "0.80191     4     5573 0.5\n",
      "1.27013     5     10981 0.5\n",
      "0.76162     6     12518 0.5\n",
      "0.83484     7     9565 0.5\n",
      "0.84761     8     2848 0.5\n",
      "0.8676     9     6749 0.5\n",
      "1.02367     10     12130 0.5\n",
      "1.0553     11     11208 0.5\n",
      "0.72217     12     10854 0.5\n",
      "0.79801     13     3697 0.5\n",
      "1.26553     14     10774 0.5\n",
      "1.15209     15     2884 0.5\n",
      "1.21938     16     8735 0.5\n",
      "0.98371     17     11412 0.5\n",
      "1.24546     18     9666 0.5\n",
      "1.08797     19     14588 0.5\n",
      "1.05898     20     14779 0.5\n",
      "0.9979     21     8674 0.5\n",
      "1.29486     22     9189 0.5\n",
      "0.95263     23     9877 0.5\n",
      "1.05254     24     7690 0.5\n",
      "0.99749     25     10975 0.5\n",
      "1.00475     26     12082 0.5\n",
      "0.99556     27     7498 0.5\n",
      "0.71853     28     6000 0.5\n",
      "1.06584     29     8610 0.5\n",
      "0.97631     30     14825 0.5\n",
      "1.11346     31     9855 0.5\n",
      "0.83088     32     2235 0.5\n",
      "0.87197     33     6425 0.5\n",
      "1.08483     34     4547 0.5\n",
      "1.26762     35     14067 0.5\n",
      "0.66042     36     9147 0.5\n",
      "0.82831     37     5314 0.5\n",
      "1.13913     38     5733 0.5\n",
      "1.08857     39     1559 0.5\n",
      "0.9887     40     7816 0.5\n",
      "1.13664     41     12477 0.5\n",
      "0.88294     42     9185 0.5\n",
      "1.18316     43     6406 0.5\n",
      "1.18047     44     9006 0.5\n",
      "0.83922     45     8022 0.5\n",
      "1.14391     46     5215 0.5\n",
      "0.98748     47     2348 0.5\n",
      "1.25024     48     7471 0.5\n",
      "1.20121     49     8766 0.5\n",
      "1.07959     50     9565 0.5\n",
      "0.93042     51     8877 0.5\n",
      "0.95978     2555     176742 0.25\n",
      "0.91403     2556     102191 0.25\n",
      "0.92772     2557     295291 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.92908 0.12051\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "17caead2-8aba-4fc5-817e-53d440c5a704",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c5d76a90-4441-4e96-b9a7-7de433686812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pre-squaring HD vtd lists to file\n"
     ]
    }
   ],
   "source": [
    "print(\"saving pre-squaring HD vtd lists to file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigNotSquaredVTD.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1604\n",
      "working on avg unit-to-HDcp dist for HD 2415\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f179943a-ef19-4036-a8c6-5ab7b1f97bf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ECACQQoQAAClECACQQoQAAClECACQQoQAAClECACQQoQAAClECACQQoQAAClECACQQoQAAClECACQQoQAAClECACQYmj2AAxeE5Y8fMg1ry+7dAAmASCDIyEAQAoRAgCkECEAQAoRAgCkECEAQAoRAgCkECEAQArfE9JPffmOCwDgwBwJAQBSOBLCEc23qgLULkdCAIAUg+5IiP9nDQBHBkdCAIAUIgQASDHoXo7pCx+/BYDqcyQEAEjhSAhHPW82Bjg6iRAGBaECcOTp18sxt99+e0ycODGGDx8eTU1N8cQTT1R6LgCgxpV9JOS+++6LRYsWxe233x7nn39+3HHHHTFnzpzYsmVLjB8/vhoz9pk3lHI4HC1hIHiewf8p+0jIzTffHN/4xjfiqquuijPPPDNuueWWaGxsjOXLl1djPgCgRpV1JOT999+PjRs3xpIlS3pdPnv27Hjqqaf2e53u7u7o7u7uOd/R0REREZ2dneXOekh7uv9d8duE/zT+2/dX5Haev/7zFbmdSpr8w/855JpKzT2Q9zWQ+vJ39UU1/vcRDtfe52VRFBW7zbIiZMeOHbF79+446aSTel1+0kknxdtvv73f67S0tMT111+/z+WNjY3l3DXUlIZbsifon4Gc+2jdRpUwmP92jnw7d+6MhoaGitxWvz4dU1dX1+t8URT7XLbX0qVLY/HixT3n9+zZE++8806MHj36gNf5T52dndHY2Bjt7e1RX1/fn3E5DLZ/Po9BPo9BLts/397HYMuWLTF27NiK3W5ZEXLCCSfEkCFD9jnqsX379n2OjuxVKpWiVCr1uuwjH/lIeVNGRH19vSdfIts/n8cgn8cgl+2f7+Mf/3gcc0zlvue0rFsaNmxYNDU1xdq1a3tdvnbt2jjvvPMqNhQAUPvKfjlm8eLFccUVV8T06dOjubk57rzzznjzzTdj/vz51ZgPAKhRZUfIV77ylfjnP/8ZP/rRj2Lbtm0xefLk+NOf/hQnn3xyNeaLUqkUP/zhD/d5SYeBYfvn8xjk8xjksv3zVesxqCsq+VkbAIA+8iu6AEAKEQIApBAhAEAKEQIApEiPkNtvvz0mTpwYw4cPj6ampnjiiScOuLa1tTXq6ur2Of3tb38bwIlrS1tbW8ydOzfGjh0bdXV18eCDDx7yOuvWrYumpqYYPnx4nHLKKfGLX/yi+oPWqHK3v32g8lpaWuKcc86JkSNHxoknnhiXX355bN269ZDXsx9URn+2v/2gspYvXx5Tpkzp+TK45ubmeOSRRw56nUo9/1Mj5L777otFixbF97///di0aVPMnDkz5syZE2+++eZBr7d169bYtm1bz+kTn/jEAE1ce7q6umLq1Klx22239Wn9a6+9FpdccknMnDkzNm3aFNddd11861vfilWrVlV50tpU7vbfyz5QOevWrYsFCxbE+vXrY+3atbFr166YPXt2dHV1HfA69oPK6c/238t+UBnjxo2LZcuWxYYNG2LDhg1x8cUXx2WXXRYvvPDCftdX9PlfJPr0pz9dzJ8/v9dlZ5xxRrFkyZL9rn/88ceLiCj+9a9/DcB0g09EFKtXrz7omu9+97vFGWec0euyb37zm8W5555bxckGh75sf/tA9W3fvr2IiGLdunUHXGM/qJ6+bH/7QfUdf/zxxa9+9av9/lsln/9pR0Lef//92LhxY8yePbvX5bNnz46nnnrqoNedNm1ajBkzJmbNmhWPP/54Ncfkv/zlL3/Z5zH7/Oc/Hxs2bIgPPvggaarBxz5QPR0dHRERMWrUqAOusR9UT1+2/172g8rbvXt3rFy5Mrq6uqK5uXm/ayr5/E+LkB07dsTu3bv3+eG7k046aZ8fyNtrzJgxceedd8aqVavigQceiEmTJsWsWbOira1tIEYmIt5+++39Pma7du2KHTt2JE01eNgHqqsoili8eHHMmDEjJk+efMB19oPq6Ov2tx9U3nPPPRcjRoyIUqkU8+fPj9WrV8dZZ52137WVfP6X/bXtlVZXV9frfFEU+1y216RJk2LSpEk955ubm6O9vT1uvPHGuOCCC6o6J/9nf4/Z/i6n8uwD1XXNNdfE5s2b489//vMh19oPKq+v299+UHmTJk2KZ599Nt59991YtWpVzJs3L9atW3fAEKnU8z/tSMgJJ5wQQ4YM2eeox/bt2/cprIM599xz46WXXqr0eBzAxz72sf0+ZkOHDo3Ro0cnTTW42QcqY+HChfHQQw/F448/HuPGjTvoWvtB5ZWz/ffHfnB4hg0bFqeddlpMnz49WlpaYurUqXHrrbfud20ln/9pETJs2LBoamqKtWvX9rp87dq1cd555/X5djZt2hRjxoyp9HgcQHNz8z6P2Zo1a2L69Olx7LHHJk01uNkHDk9RFHHNNdfEAw88EI899lhMnDjxkNexH1ROf7b//tgPKqsoiuju7t7vv1X0+V/2W1kraOXKlcWxxx5brFixotiyZUuxaNGi4rjjjitef/31oiiKYsmSJcUVV1zRs/5nP/tZsXr16uLFF18snn/++WLJkiVFRBSrVq3K+hOOejt37iw2bdpUbNq0qYiI4uabby42bdpUvPHGG0VR7PsYvPrqq8WHP/zh4tvf/naxZcuWYsWKFcWxxx5b/O53v8v6E45q5W5/+0DlXX311UVDQ0PR2tpabNu2ref073//u2eN/aB6+rP97QeVtXTp0qKtra147bXXis2bNxfXXXddccwxxxRr1qwpiqK6z//UCCmKovj5z39enHzyycWwYcOKT33qU70+ljVv3rziM5/5TM/5G264oTj11FOL4cOHF8cff3wxY8aM4uGHH06Yunbs/ajbf5/mzZtXFMW+j0FRFEVra2sxbdq0YtiwYcWECROK5cuXD/zgNaLc7W8fqLz9bf+IKO66666eNfaD6unP9rcfVNbXv/71nv8Of/SjHy1mzZrVEyBFUd3nf11R/P93kwAADKD0r20HAAYnEQIApBAhAEAKEQIApBAhAEAKEQIApBAhAEAKEQIApBAhAEAKEQIApBAhAEAKEQIApPh/4Zs2OQg7C84AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Restart\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 2309 HDs with pop < 757484 to within 3059\n",
      "working on squaring up HD 0 time is now 0\n",
      "working on squaring up HD 122 time is now 65\n",
      "working on squaring up HD 235 time is now 85\n",
      "working on squaring up HD 341 time is now 91\n",
      "working on squaring up HD 457 time is now 95\n",
      "working on squaring up HD 559 time is now 106\n",
      "working on squaring up HD 689 time is now 137\n",
      "working on squaring up HD 813 time is now 214\n",
      "working on squaring up HD 948 time is now 222\n",
      "working on squaring up HD 1048 time is now 225\n",
      "working on squaring up HD 1166 time is now 251\n",
      "working on squaring up HD 1268 time is now 300\n",
      "working on squaring up HD 1396 time is now 307\n",
      "working on squaring up HD 1593 time is now 317\n",
      "working on squaring up HD 1708 time is now 321\n",
      "working on squaring up HD 1808 time is now 329\n",
      "working on squaring up HD 1908 time is now 333\n",
      "working on squaring up HD 2022 time is now 380\n",
      "working on squaring up HD 2128 time is now 434\n",
      "working on squaring up HD 2232 time is now 458\n",
      "working on squaring up HD 2339 time is now 472\n",
      "working on squaring up HD 2440 time is now 485\n",
      "working on squaring up HD 2551 time is now 515\n",
      "working on squaring up HD 2682 time is now 523\n",
      "We have addressed underpop in a total of 2309 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.92908 0.12051\n",
      "amped unit avg and sd usage are 1.00466 0.10322\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 161 overPopped HDs to within 3059\n",
      "working on squaring down HD 63\n",
      "working on squaring down HD 597\n",
      "working on squaring down HD 746\n",
      "working on squaring down HD 1302\n",
      "working on squaring down HD 1935\n",
      "working on squaring down HD 2652\n",
      "We have addressed underpop in a total of 2309 HDs\n",
      "We have addressed overpop in a total of 161 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%30 == 0:\n",
    "        print(\"working on squaring down HD\",t)\n",
    "    #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "    excess = HDvPop[t] - aDP\n",
    "    origExcess = excess\n",
    "    HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "    barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)\n",
    "\n",
    "    for u in set(HDunitList[t]).difference(barredSet):\n",
    "        isBoundary = False\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t]:\n",
    "                isBoundary = True\n",
    "                break\n",
    "        if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "            HDboundaryList.append(u)\n",
    "            HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "    currList = HDunitList[t].copy()\n",
    "    shedList, stillGoing = list(), True\n",
    "    while excess > maxGap and len(HDboundaryList) > 0:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "        while i < len(HDboundaryScore) and notYetPicked:        \n",
    "            listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "            unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "            tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "            unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "            if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #add this eligible unit\n",
    "            shedList.append(unitNoToShed)\n",
    "            excess -= unitPop[unitNoToShed]        \n",
    "            del currList[currList.index(unitNoToShed) ]\n",
    "            del HDboundaryList[listNo]\n",
    "            del HDboundaryScore[listNo]\n",
    "            for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                  u not in barredSet): \n",
    "                    HDboundaryList.append(u)\n",
    "                    HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "            for uu in HDboundaryList.copy():\n",
    "                if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                    del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                    del HDboundaryList[HDboundaryList.index(uu)]                \n",
    "    for u in shedList:\n",
    "        unitUse[u] -= HDweight[t] * nDistricts\n",
    "    HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "             np.sum([unitPop[u] for u in shedList]) )\n",
    "    \n",
    "    HDnShedUnits[t] = -1 * len(shedList)\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 0.92908 0.12051\n",
      "amped unit avg and sd usage are 1.00466 0.10322\n",
      "shed unit avg and sd usage are 0.99888 0.10231\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2698\n",
      "working on HD 300 out of 2698\n",
      "working on HD 600 out of 2698\n",
      "working on HD 901 out of 2698\n",
      "working on HD 1202 out of 2698\n",
      "working on HD 1503 out of 2698\n",
      "working on HD 1804 out of 2698\n",
      "working on HD 2104 out of 2698\n",
      "working on HD 2415 out of 2698\n",
      "all done checking HD and complement contiguity for all 2698 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 2309\n",
      "overpop contigFail, enclaveFail = 0 0 out of 161\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 2555 with pop 176742.0 now has use 0.95978\n",
      "CCB unit 2556 with pop 102191.0 now has use 0.91779\n",
      "CCB unit 2557 with pop 295291.0 now has use 0.92772\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"unpatchedContigGoodPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of units\n",
      "orig unit avg and sd usage are 0.99944 0.12336\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 300\n",
      "working on HD 600\n",
      "working on HD 900\n",
      "working on HD 1200\n",
      "working on HD 1500\n",
      "out of 1652 total HDs, there were 1652 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse for county clusters\n",
      "6793 0.9733880685005182 82874.0\n",
      "6794 0.9960002441574092 90352.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,unitUse[u], unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.065\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.935\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMinUse value 0.935\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 378 units out of 2558 with usage below 0.935\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 22954\n",
      "current avg and SD of unit usage are 0.99922 0.07203 . Now trying to increase up to 13 units' underusage\n",
      "all done trying to reduce usage of unit 1263 final usage = 0.93972 3 sec elapsed 45 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99922 0.07112\n",
      "all done trying to reduce usage of unit 1368 final usage = 0.93902 8 sec elapsed 34 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99921 0.07022\n",
      "all done trying to reduce usage of unit 2428 final usage = 0.84106 13 sec elapsed 20 0 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99922 0.06975\n",
      "all done trying to reduce usage of unit 2445 final usage = 0.84515 18 sec elapsed 25 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99922 0.06941\n",
      "all done trying to reduce usage of unit 1251 final usage = 0.9408 27 sec elapsed 38 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99923 0.06841\n",
      "all done trying to reduce usage of unit 386 final usage = 0.93591 39 sec elapsed 52 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99923 0.06792\n",
      "all done trying to reduce usage of unit 1247 final usage = 0.93989 45 sec elapsed 31 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99923 0.06722\n",
      "all done trying to reduce usage of unit 843 final usage = 0.93075 51 sec elapsed 31 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99924 0.06646\n",
      "all done trying to reduce usage of unit 2431 final usage = 0.88781 59 sec elapsed 33 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99924 0.06596\n",
      "starting to increase usage for unit 387 with usage 0.80266 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 387 final usage = 0.93165 68 sec elapsed 33 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99926 0.06561\n",
      "all done trying to reduce usage of unit 2489 final usage = 0.93588 72 sec elapsed 19 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99926 0.06498\n"
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Works fine.  For some states, may want to do overpopped first; DANGER OF LONG STRINGS\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the avg no of units per district\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4713523d-5dae-41aa-a2c2-fb4abd0983c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "691 has pop, use 0.348 0.74698\n",
      "700 with pop, use 1.288 0.74698 is a neighbor of stubborn 691\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 691\n",
      "692 has pop, use 0.39 0.74698\n",
      "700 with pop, use 1.288 0.74698 is a neighbor of stubborn 692\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 692\n",
      "693 has pop, use 0.385 0.74698\n",
      "700 with pop, use 1.288 0.74698 is a neighbor of stubborn 693\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 693\n",
      "694 has pop, use 0.385 0.74698\n",
      "700 with pop, use 1.288 0.74698 is a neighbor of stubborn 694\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 694\n",
      "695 has pop, use 0.149 0.74698\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 695\n",
      "696 has pop, use 10.459 0.74698\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 696\n",
      "697 has pop, use 0.164 0.74698\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 697\n",
      "698 has pop, use 0.206 0.74698\n",
      "703 with pop, use 16625.966 0.74698 is a neighbor of stubborn 698\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in [691, 692, 693, 694, 695, 696, 697, 698]:\n",
    "    plotPoly(unitGeom[u])\n",
    "    print(u,\"has pop, use\",r3(unitPop[u]),r5(unitUse[u]))\n",
    "    for uu in unitNbrs[u]:\n",
    "        if uu not in [691, 692, 693, 694, 695, 696, 697, 698]:\n",
    "            plotPoly(unitGeom[uu],0.2)\n",
    "            print(uu,\"with pop, use\",r3(unitPop[uu]),r5(unitUse[uu]),\"is a neighbor of stubborn\",u)\n",
    "for c in range(nCounties):\n",
    "    if u in countyUnitList[c]:\n",
    "        #plotPoly(countyGeom[c])\n",
    "        pass\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4942adcc-7281-4eea-a8f8-8ddce84e3524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "special for GA -- rerun above but with [691, 692, 693, 694, 695, 696, 697, 698, 700, 703] as the UUUc\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 22954\n",
      "current avg and SD of unit usage are 0.99917 0.07323 . Now trying to increase up to 1 units' underusage\n",
      "starting to increase usage for unit 691 with usage 0.74698 . Total UU units tried = 1\n",
      "try to add unit 691 from 0 th HD.  Usage up to 0.7469819569024434\n",
      "try to add unit 691 from 20 th HD.  Usage up to 0.8443933611079214\n",
      "try to add unit 691 from 40 th HD.  Usage up to 0.9347406643147733\n",
      "all done trying to reduce usage of unit 691 final usage = 0.94401 3 sec elapsed 40 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99922 0.07203\n"
     ]
    }
   ],
   "source": [
    "print(\"special for GA -- rerun above but with [691, 692, 693, 694, 695, 696, 697, 698, 700, 703] as the UUUc\")\n",
    "UUUc = [691, 692, 693, 694, 695, 696, 697, 698, 700, 703]\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "maxNtries = 1 # int(currSD * nUnits / nDistricts)   #GA special\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    UUU = UUUc[0]  #GA special\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99888 0.99926 and their SDs are 0.10231 0.06498\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2698\n",
      "working on HD 300 out of 2698\n",
      "working on HD 600 out of 2698\n",
      "working on HD 900 out of 2698\n",
      "working on HD 1200 out of 2698\n",
      "working on HD 1500 out of 2698\n",
      "working on HD 1800 out of 2698\n",
      "working on HD 2100 out of 2698\n",
      "working on HD 2400 out of 2698\n",
      "all done checking HD and complement contiguity for all 2698 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for intervention is 1.12\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 200 units out of 2558 with usage above 1.12\n",
      "maxExchangePop is currently 0.03 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 22954\n",
      "current avg and SD of unit usage are 0.99915 0.08601 . Now trying to reduce up to 10 units' overusage\n",
      "starting to reduce usage for unit 529 with usage 1.30508 . Total units tried = 1\n",
      "try to drop unit 529 from 15 th HD.  usage down to 1.2344728875563695\n",
      "try to drop unit 529 from 30 th HD.  usage down to 1.196308444769901\n",
      "try to drop unit 529 from 45 th HD.  usage down to 1.1624543452014624\n",
      "try to drop unit 529 from 60 th HD.  usage down to 1.129121721358918\n",
      "all done trying to reduce usage of unit 529 final usage = 1.10349 2 sec elapsed 38 0 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99914 0.08451\n",
      "starting to reduce usage for unit 1232 with usage 1.28769 . Total units tried = 2\n",
      "try to drop unit 1232 from 19 th HD.  usage down to 1.1682964416795203\n",
      "all done trying to reduce usage of unit 1232 final usage = 1.11785 4 sec elapsed 28 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99914 0.08317\n",
      "starting to reduce usage for unit 2514 with usage 1.25714 . Total units tried = 3\n",
      "try to drop unit 2514 from 16 th HD.  usage down to 1.201010874999927\n",
      "try to drop unit 2514 from 32 th HD.  usage down to 1.1845523691949873\n",
      "try to drop unit 2514 from 48 th HD.  usage down to 1.1814039104890326\n",
      "try to drop unit 2514 from 64 th HD.  usage down to 1.1814039104890326\n",
      "try to drop unit 2514 from 80 th HD.  usage down to 1.1814039104890326\n",
      "all done trying to reduce usage of unit 2514 final usage = 1.1814 8 sec elapsed 6 0 successful, failed patches, couldn't start= 76\n",
      "current avg and SD of usage are 0.99915 0.08255\n",
      "starting to reduce usage for unit 2495 with usage 1.22344 . Total units tried = 4\n",
      "try to drop unit 2495 from 15 th HD.  usage down to 1.1733491363068764\n",
      "all done trying to reduce usage of unit 2495 final usage = 1.11383 17 sec elapsed 14 0 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99914 0.08195\n",
      "starting to reduce usage for unit 343 with usage 1.24457 . Total units tried = 5\n",
      "all done trying to reduce usage of unit 343 final usage = 1.1153 18 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99913 0.08122\n",
      "starting to reduce usage for unit 14 with usage 1.21287 . Total units tried = 6\n",
      "all done trying to reduce usage of unit 14 final usage = 1.11982 19 sec elapsed 20 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99914 0.08053\n",
      "starting to reduce usage for unit 2515 with usage 1.20871 . Total units tried = 7\n",
      "try to drop unit 2515 from 22 th HD.  usage down to 1.1563338669449146\n",
      "all done trying to reduce usage of unit 2515 final usage = 1.11516 22 sec elapsed 27 1 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99914 0.07978\n",
      "starting to reduce usage for unit 101 with usage 1.20949 . Total units tried = 8\n",
      "try to drop unit 101 from 22 th HD.  usage down to 1.150278736523703\n",
      "try to drop unit 101 from 44 th HD.  usage down to 1.1244375885228084\n",
      "all done trying to reduce usage of unit 101 final usage = 1.11843 24 sec elapsed 20 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99913 0.07906\n",
      "starting to reduce usage for unit 1105 with usage 1.21823 . Total units tried = 9\n",
      "all done trying to reduce usage of unit 1105 final usage = 1.11312 25 sec elapsed 15 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99912 0.07862\n",
      "starting to reduce usage for unit 2534 with usage 1.20869 . Total units tried = 10\n",
      "try to drop unit 2534 from 14 th HD.  usage down to 1.1274435889478063\n",
      "all done trying to reduce usage of unit 2534 final usage = 1.09314 32 sec elapsed 15 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99912 0.07809\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  WATCH FOR CREATING LONG STRINGS\n",
    "maxSD = 0.06 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for intervention is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "\n",
    "nBigUsers = 5\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.18\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for FL, above is second run-through after passing first 10 thru without trying OUU alone-shed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99888 0.99912 and their SDs are 0.10231 0.07809\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2698\n",
      "working on HD 300 out of 2698\n",
      "working on HD 600 out of 2698\n",
      "working on HD 900 out of 2698\n",
      "working on HD 1200 out of 2698\n",
      "working on HD 1500 out of 2698\n",
      "working on HD 1800 out of 2698\n",
      "working on HD 2100 out of 2698\n",
      "working on HD 2400 out of 2698\n",
      "all done checking HD and complement contiguity for all 2698 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211 7213 7211 7211 7211 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999998006 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "14 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999999999999884\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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invrxdVLtiOI+e+RWqXZEcT8bIXcVXRT36a9vlmpHFPfZI7dItSOKmze2n1Q7MnGy9a1EcZZQs3A/yxyT6td6KwG/DbRgwQL893//N/785z/j4MGD+M1vfoOioiLMnTvX7305/KxcjQdRXL5kDQNR3N8fkftQieLGXS23A4vinrtnsFQ7orjoiFAM6qGfZA7qESsspjX6mm7CD4sl1IzR13TTjXnmTrnkQBQ3R/KgI4obn54k/H5k+lecnh5d5K6siOJ6d5W7EiaKm3dzf6l2RHFx0RZ0ExQA6xZtERYJe+1nN0j1RxT348FySaEobs4tcttHFPejtESpb8U/Smv76rVTpCUE49MSdGPGpyUIi59dFRcJi+CqpCXEhKvi9L/mLJnivgaUJ3Gq+pOa0ElqO6cmdNKNmf8jufddJm5kn67oLFhwt0tUGEZKXDVbdHsa5oxNbfUazSbPa0apEPBk5Z577sHLL7+Mp59+GoMHD8aWLVuwceNG9O7d2+99sYSaMWesfjY5Z6w4m1R1Mu6XFC110uonuF0SGyV3GV8UlxQrd9tFJm7d/DFut5FsEa0QswmvTh+sG/Pq9MHCCpCqDsqq9p8Qswmvz9C/qvT6jKHC1zUiNQ7JgltgydbGKpn+aEfV9gGA3U+Od5uwyBYHU/U5DTGbsFzwfi2XeL9U7j+v/Uy/P6/9TNwfAFj1wHC3nw1Pip4dXnq72wTBEmLC4aW3C9tQuf+o6A8AHMu5w23CYjY1Pi+i8nWFmE14ftpA3ZicaQOlq+Iuuj0N3z0zCU/dMQAPZPTGU3cMwHfPTPJ7ogIYpIKtN3yx6rKqCqSqVl1OXbjBbTXCQokKm1sPn8X9fxYvJfDuz0dgzNXur0LYHRpG/36z7jz+ZGsEtj02TvrDoKo8+eJP9qO06vKgsYToMDx958CAVKJUtf/k5hfjqU/246zL6+oWHYZnPHhdqqpZqq6KqWL7AGrKrqv6nDa+Xwdw1mWmRUJMOJ6eel1AKinn5hdj8doDKK283J/E2HBkT/GsP4C6cvKny2sw6dUvcbHOjk7hIfj01zcLr2C0pHL/UdEfACgsvYiJr3yJOruG8BATch++WXhFpSWV1WLbWrLhSsvt+5In528mK26oWg9BxckYAI6UVGHSq1/ikgMIMzfeTxVdUXGyOzQMzPoM1TrDyTtZQrAv6zZhkuE8aQFoVQYe8OykpZLR1vhQtf+oeF2q1gkJxvVGnFR9To22RpWq/hiN0fYfVVS+rvbw3jNZoVb0ysADnn0rUXnSIv8w2oKRRERMVqgZX9y+4UmLiIi84cn52/PrnOQRI5zUPVkvQnbqmypG2D4doT9Ga8douH3IG3zffY/Jihuqxgo89fE+nL14ueJkt06heOYnni35DXg3kLC0Uj9R8TSurYF7VzKQMDe/GEvW5uOHystlvxNjLMiemh6QAYCqBuqq0tYguaTYCGRNCcxYk8b36wB+cB2wGROObA/fd8BYYzsat/MBlLgs7JcUG44sDweiqrw9arTkyWhjRIy0fYz4vgcj3gZqg4qdz7nktzvLPRiI6m5ZdNkpmjuOluHeVeLF1t7PHCm8sqLqdancPipm8ajsD+B98qRyO7c1i8fTAdEqt4+qJFXF7AmjbWdnW0YaEK1ylooKRto+Rnzf2xOOWfGCip3P7tBwzZOf6q4sHGo24dCzk4RZs7tExUkmYamqbUB61me6MUBjkTq9GRB2h4b+T2zUXY3TbAK+X3q77utSuX3cJSpOMgmL3aFhwOJc3bVALKFmHHx6otS3HG+TJ7tDw6Csz3BRb/ZWeAj2LdGfvSUaq2QCkCQxVkn1bDIVyYGKAeN2h4Zhz27ChWr366R0jgrDN0+O98t2BtQmlyraUTkwXwUjbR8jvu/tjSfn7/Y/10shu0ND9vqCNmtJOB/LXl8Au2Dt7C0HS3VPxADQ4NCw5aD7kyzQeOtHL1EBgLNV9SgXxMx7Z7vu87Jx/1fwg9Sy4f9XoL/K6JZDktvnkP72qam36yYqALCpoBQ1ghXAth06K1y0rL7BgW2HzurGAPrJ06aCUmS+s1vYxvYj53QTFaBxUcXtR87pxojGKmm4PFZJ1B+9RAUALtaL+2N3aFjw4be6MQs+/Fb4+apvcGDVVv0VsFdtLRS+pzuPlukmKgBwofoSdh4t041RtZ1VHX9UtaNqO6titO1jtPc92DFZcaFq53vx/w5L/T1R3PSVckmGKG7rsUqpdkRxi9flS7Ujintxk+T2EcQ9t7FAqh1R3MqtR6XaEcWpSp7W5J2S6o8oTtVYpY8k+yOK2/69OOmprrdj+/f6Sc+7O45LJc3v7jiu359j+n9HNk7VdlZ1/FHVjqrtrIrRto/R3vdgx2TFhaqd7wfJdkRxpZX6V0w8jfNWZa3c0vSiuArJZcxFccfLqqXaEcUdPXdRqh1RnKrkqapObjuL4hJi5JZHEMVV18v1RxT30R7JpEcQd6Jc7n0XxZ05XyPVjihO1XZWdfxR1Y6q7ayK0baP0d73YMdkxYWqnS9Rsh1RXEKM3Gwf2ThvWSPlJo+J4q6RrLwrikvpKrdQnyguVvJ1ieJUJU+JsXJrOYninGv6uLtbboLcmj7DU+Sms4viRFdVZON6x8m976K47p3lyqqL4lRtZ1XHH1XtqNrOqhht+xjtfQ92TFZcqNr5fvujq6X+nijur7NHSbUjivvdv8ktCimKm5Cuv9ifbNzL9+gvsiYb97jkwD5R3LTBPaTaEcWpSp6G9tLfv2TjQswmLJnc+Npb7tPO35dMThMO/ps5KgUmwbhik6kxTs/wFLnXJYq7PyNFarXb+zP0+zOqb7xUf0RxqrazquOPqnZUbWdVjLZ9jPa+BzsmKy5U7XxjByQgVBATajZh7AD9k3pctMXtyrJO3aItwnorc8ddp/u8bFy0Re4bvyhO1Wq3qlZL/vmYPrrPy8apSp6SJb/xy8RNTE/G6zOGtroKkxgbLj3DwBJqxuwx+qvCzh4jXhVWVdJjCTUjU9CfTIn+jOzbFZ2jwnRjOkeFYaREoUTndk5qsTp1kjVCejurOv6oakfVdlbFaNsHMNb7HuyYrLSgaudbdt8Q3Zhl9w2R2vl2PznebcIiW2dF1RL2stVtZeLWzR/jNmHxZLVbFUvYW0LNUkmP6KCsKnlyftPS4+k3LbvDofu7yKLb03S3s8z0VVVJj7M/c8amtvrmbzbJT6cNMZvw/LSBujHPTxsofZKYmJ6MbY+Nw/uZI/HK9MF4P3Mktj02zqMppyqOPyrbcW7ntk6i/p62DBhv+zjbMsr7HsxYwbYNE9OTMe7aRK8qNk5MT8byGUPx+P/sQXnt5SH1cREmPPfvQzza+Z65Mx0L/7YHF+out9M53IRn7kyXbkOFkX26IsoSIqy3MbKPXFLjbgCt7ABcp7uG9sC+UxX4waUCaWJsOO4aKnd7x+7QkH/aphuTf9oGu0MTnrhWPTAcU5Ztxb5Trdsb1CNWKnkKMZsw5fpk3foWU65PljqJuqtrcrbqEuauzvOo6JnelOzc/GKpdhbdnoY1eafdFjn05OQ3pFcXxHc61azicHynMAzp1UW6jYnpyRifluC2Ls6VVCAtOFPRdNwY1ruLx9+IG/uU5HUlUxXHMaBxOyfGFjerpJwYG+HRdnZSUWnaaNsHaPzMertUicr+BCMWhWuDqkqCRqmsKlP8qktUGL6WKH6lqjjYzS9sxoky97MseneNxJe/G6fbBqBm+xitwq+qonkqi/ipKprnLpFzkr2qZqTicq5tBVulV5XFylQcD1UyWsVYVcs+tCcsCucF54ez5bz3kopazFudh9z8Yql2VBQHszs03QMyAMxdnScsFrTzmLj41fnqS9h5TL/41c6jZVLFwURFtCqqL+kmKgBwoqwGFYI+2x0aFq7ZrxuzaM1+4fZRNXVQVdEzVUXz/nFQrojfPw7qF/FTVTSvqrZBN1EBgH2nbKgSTH2Xed8XSrzv9Q0OrBQUPVspWfTMmfS0/JMODVixpRA5ktPaVVFxHFNZrEzF8VAlVcd5lf2ZuzqvWaICACW2OswNQH+MiMmKC9GHU4Pch1NVcbDP9sntoKK4vx+Ua0cUt+17cQVXmbj/eFN8FUMmTlUS1ilM7m6oKE5V0bMXPv9Oqj+iuMVr9U/osnHLtxyRakcU9/Bf9RNv2ThVlWff3n4couvKmtYYpydYK72qKlam6nioitEqxqpKvoMdkxUXog8nIPfhfOxve6X+niju0f/ZI9WOKO6TPXLJiihu3+kLUu2I4r4vlSvCJorbITgZyca99g+5irqiOGVFzyTrtYjiTtvkigWK4r77oUqqHVHcPsG4INm4HZKVZ0Vxu4/LVQQVxQVrpVdVVxxVFUtUxWgVY1Ul38GOyYqL4gtyFS1Fcev2l0i1I4qrliscKoyrrpMbsCqKKzonV7ZfFGcWzV+VjpP9pqEfd7xc7n0XxakqemYJkRtwKBvnrQa73BUBUVx4qNz7Lo6THUipHxclObBTFBeslV5VFStTVSxRFaNVjFWVfAc7Jisu9pw8rzTOKOokr66K4k5WyCU9orjR/eSm3IriMvrIFfUSxVlC5D4GojhVRc9uuy5Rqh1RXFyU3MlYFJcoWSFZFHffiF5S7YjiVE2hv2uI3GwxUVywVnpVVaxMVbFEVYxXMVZN8h3smKy4UPM9nUSmD09REqeqqNfk6+VG2oviVBU9WzJFbkq6KG7jr2+RakcU9+83yCUZorhfjOkr1Y4obmQf8fveJSpMOIV+VP944VWTKEsIRvXXT3aDtdKrqmJlqoolqmK0irEq61cFMyYrLkJD5C5ByMZR21SdJFQV9brlarkrGaI4VUXPVBWXS+ocgcgwwd8KMyOps/43yJ+PlqzwK4izhJoxZ6z+9pkzVrx9ZN73HIn3PcRswks/vV435qWfXt9hK70CaoqVqdqfVTFaxVhVyXewY7Li4q2vTiuJG9pT7nKmbJy3+sR3UhI3ZVA3qXZEcapOEsDl4ntJLcrJJ8WGS9faUFl2XUVlVUBNZV4AOPjMJLcJS2SYGQefmSRsQ1WSAVzePu7akN0+l9/35ifRZGuE9Pvu2k7LW1iJMRaP2lH1vqtitAqtqvZnVYxUMVZV8h3sWBTORcrCDdKxx5+/w+1zaU99iupL4kGJUWFmFOicLFT15/XN3+P3n4tnvDw24WrMG9ff7fNvbD2GZzYcFLbz1B0DMEtivZ3c/GIsWZuPHyovz0ZJjLEge2r6FVUO9aaipaoiY071DQ4llShVVPwEgJILtfjxn7bAVtuA2IhQ/O+vxgqvqLSUs7EAK7cUNrsNagIw+wpOxqq2j7fvu+p2VL0uVVS9LlVU7c+qGGn7NBaFK2hWKTiQRer8wZPzN5MVF6qSg2ue2CA1qDU8BDi01H07qvqz9dBZ3P/mLmEb7/7HCIy5xv1VkfoGB6596lNhRdTvnpkkfYA23sGiY1WQ9JTRTsZEwcRIx0N/8OT8zbWBfKBXXCd8f1ZcS6RXnNztGW+V18jV2xDFOe/N65Un9/TevIo1NVRRteZIMLOEmqWumhGR54x0PDQafiVykXVbPyVxH8wZJdWObJy3VE7VU31v3u7QsONoGdbuPY0dR8sCXqXRebCYOvgqZPTtykSFiMgAeGXFxYO3XoOsz8SlxR+89Rrd5+OiLYiNCIVNZ42T2IhQxEXL1a/wlnOqXklFbZvTrk1oHFgmO1Vv0e1p+O2Ea72+HZCbX4zFn+xvtmpuQnQYnr5zoN/HrAQ7o20fo/VHlWB9XUSBxmSlhTlj9W9ziGZFAI0HrE7h+slKp/BQ2B2aXw5kzql683QGkHo6VS/EbEJadyviY8KREBPh8etwN6C1tOoS5q7O82hAa2PSk4/Sqsu3sRKiLXj6Tv8P1DUilQOZVfUnGMcGGW0VX6JgwgG2LlQNIN1xtAz3rhIv1vd+5kjd+5OqBtg6qVqiva1R60mxEciaIndQtjs0DFicq7uwmyXUjINPTxQmCipn8ag82RhltovqWU5G649ROFfxbXnocG4Zf0+HJWoPOMD2CnmyIJneIENVa0/MGtULb2wvErYza5S4wmjOxgLdJdpzNhZIjTdxd7IpsdVKXxHZduiscAXa+gYHth06i5sHuC8mZXdomP+e/iKO89/bg0PPJkklPW2dbIr/tWS8JyebnI0FWLW1sNm+tHTjQWSO8WxMj7fJk92hYcGH3+rGLPjwW4xPE28fVf2RWV1Wtj9GIVrF14TGVXzb2+siMhIOsHWhakEyVQNab7k2SaodUZyqJexlTjaLJJYyX7n1qO7zsnFbvitFg+BvNTg0bPlOf3l6vZMN0HjCkV0yPmdjAVZsKWyV9Do0YMWWQuRIrizrTJ5arg5b8q/kKTdfvJL29u/PCRdNrK63Y/v34gXSVPQnWFeXNdoqvkTBiMmKC1ULkqlae6K8WnLKsSBO1RL2O4+JTzbnqy9h5zH9k83Rc+Jp3TJxL/79kFQ7ojjRyQaQO9moTAr1vqnLJk//k3dS93nZOFF/INmfYF1d1mir+BIFIyYrLlQtSOYc0Kr3TV1mQKuqKzSqrhjtkPzGK4qLjZS7+yiKcx0w6k1cSUWNVDuiOFVJoark6fQFudclilN35SA4V5c13iq+RMEnYMnK8ePHMWvWLKSmpiIyMhJ9+/bFkiVLUF8vdwLyBZULkn2Ud8qr5wFgWO8uwhiZuJ5d5K4YiePUrEt95/VXSbUiikuMCdd9Xjau/KLkFSxBnKqk0HXgsjdxPSTfd1GcqisHwbq6rNFW8SUKRgFLVr777js4HA6sWLECBw4cwB//+EcsX74cjz/+eKC6BAAY0kv/xC96Hmhc/8LdYFanTQWlqBGMJ9hxRPKyuSDu2sQYqXZEcRl99FdBlo27Ltkq1Y4o7rf/drVUO6K4uGi5pEcUp+o2YnlVne7zsnF3De0h1Y4oTtWVg2BdXdZoq/gSBaOAJSsTJ07Em2++iQkTJqBPnz6YMmUKHn30UaxZsyZQXVI2gPTJj/VnYMjGvfp/30m1I4orsUne5hDEDZW80iOK23VCbqChKG7stQkIFZwAQs0mjL1Wf3n6liv3XmmcqtuIcZ3kigWK4kb1i0eUYJG4TpYQjOqnn1yqunIQzKvLGmkVX6JgZKgxKxUVFYiL0z/g1dXVwWazNftRRdUA0k/2iGdGyMR9c7JSqh1R3Cff6A/6lI17Z4dcO6K4M5JjKURxIWYTlt03RDdm2X1DhCc/58lYj8zJWNVtxCRrpO7zsnEhZhNe+un1ujEv/vR64fZReeVgYnoyls8Y2irxS7ZGtNsaK04T05Ox7bFxeD9zJF6ZPhjvZ47EtsfGtevXRGQUhklWjh49ij/96U+YO3eublxOTg6sVmvTT8+ePZX1YftRudsuojiJBZc9ivPW9uNVSuI+O/CDVDuiOFUnY+DyyS+hxdIFiTEW6ZOf82RsQtsnYxPkT8Yq1k5SlTwBjdtHrz+yJ1KVVw4mpifjq4XBeVLn2lJEvqG8KFxWVhays7N1Y3bv3o0bbrih6fczZ85g4sSJuPvuu/GLX/xC9/8uWrQICxYsaPrdZrMpS1hOn5ecPSGIM0FuKKroMGYGoD/J9XKcHjXDYoFSm9xYClGcVXI2kGycitWSnSfjlkXPkq6ggq23aye5Lo/gbi0n2eQpN78YK7cUtmpH04CVWwoxpFcXjxIWVatSc3VZIvKE8mRl/vz5mD59um5MSkpK07/PnDmDW2+9FRkZGVi5cqWw/fDwcISHyw2I9FT3znJjF0RxA6+Kxb7T4ttTA6/SLy88e2wqluusU+QapycUgPtViprH6ekWE4ZTErdwusXoD6L8rkTu9pZsHKDm5KfyZGwJNetWOZbpS1vJk6cVY1VXVmWSQUSBoDxZiY+PR3y83KyR06dP49Zbb8WwYcPw5ptvwmwO7F2pm/p2w2v/OCYVp+fdWSNx/dOfC9t5d9ZI4d+RSVZE/Zk8MAkf7y8RtjN5oH4l3KuTYrDnpDgJuzpJf1ZRVZ1M6iQfp5KRTsbeJk+e1EcxymsmImpLwNYGOnPmDG655Rb06tULf/jDH3D27Nmm55KS5MrMqzayb+PUSr1Btp2jwjBScGC3RoWhd9dInChzfxWid9dIWAXTOMtrJOt/COKq7DI3k8RxXaPkrjyJ4hJjJeujSMYFM2+SJ1ZWJaJgEbBLGZ9//jmOHDmCzZs3o0ePHkhOTm76CRSZqZXPS06t/PJ349C7a9sDRHt3jcSXvxsnbENVfYtOFrmcVBRnlvxGL4ob1kuuOJZsHLWNlVWJKFgELFl58MEHoWlamz+BpGr2hAqq6lvcNUSyOJggTlUF0uTOcrOBZOOobaysSkTBwjBTl40iN79Yd9VcmdVlAeDmFza7vQ10oqwGN7+wWdiGqjWGRvUXFweLsoRgVH/9sUaqKpCqnJpL7rGyKhEFCyYrLlRVsK2ovqQ7XgVoTFgqBAXoAGBP0XmvngcaT1r9EjrpxvRL6CRVHOyeG/Svvvz0hh5S7Uy5Xv8K1ZTrkz06idodGnYcLcPavaex42iZ8D3qKFhZlYiCQcAG2BqRJxVsb9IpUf7AGzul/t4Db+zE2l+Ncft8fYMDKwWzgVZuKcRvJ1yrW8Ojpt6Ofaf0Z/HsO2VDTb0dkTpXYOwODeu+1b+ytO7bYvy/iQN0Ew27Q8MHX+sv5Pjh16eE7Tjl5hd7NcU32Kmckk1EFAi8suJix1H9MvqycQVn5JYAEMW9ta11Ma+WtH/F6Vm6oUCqP6I40VRY4PJUWD2qljUAGhOVeavzWvWrpKIW81bnSd+2C3asrEpE7RmTlWbU1Hq9JNmMKC63QFwbRSbu21MXpNoRxamaCqsqKRQVPQMai57xlhARUfvGZMVFRh+5Ynaycd4qPi+3po8oLiZc7m6fKC4+Wq7uiThOTVLoSdEzIiJqv5isuHAWhdMjUxROdqOK4qrq5JY6FMWN7a9f4VY2zmGXSzJEcTemyE2BFsWx6BkRUcfAZMWFqqJwPbrIFdkSxdU2yCUHorhEyXolorh/Hpe7fSOKM4dIFpcTxKm70kNEREbGZKWFienJWD5jKJJalHpPig3Hcsmpnktuv07qb4niLKH6tVFk45Ji5ZIncZzsoEz9uHNVcqs3C+NULSdNRESGxqnLbfB2quct1yXCbEKrwnKuzKbGOD03pHTGl4fFVzNuSOms+/yI1DjhmkddosKERdgy+nbFsi+OCPsjqmCrqgz8uYuSSY9kHBERGROvrLjhzVTPELMJr/1sqG7Maz8bKmzzv+67QervycTJTBUWGZ4iV1FWFDesdxeYBJvTZGqM08O1b4iIOgYmKz7ivJ0UH9X8Fk18VIj07SS9Qm+exP19n1ytEVHcV4fP6j4vG7e7sByiJaA0rTFOD9e+ISLqGHgbyIe8vZ307o7j0nGzxvRx+/zCdflS7Sxcl4/bBnd3+/yLfz8s1c6Lfz+MW9Pc3+LaduScVDvbjpzDTTrrFTnXvpm3Og8mNB+awrVviIiCB5MVH3PeTroSJ8qrlcRV1zdItSOK+6FSbuyHKG7/6QtS7cjEOde+aVluP4nl9omIggaTFR+zO7QrvrLSOy5KSVx8dDhOXxDXGhFN8U2MseCsRMKSGGPRfT4yTG6Wk2wc174hIgpuTFZ8yNsF9u7PSMEzGw5Kxen55JejMfy5vwvb+eSXo3Wf/+2/XYP/eOdrYTu//bdrdJ8fkRqHTQdLhe14MtbEmytYREbjzZccomDEZMVHnAvstRxH6lxg73WJQbb1DQ6pv1Xf4NAdZBsdIfc2i+LGXpuAULMJDTpzskPNJoy9NkG3nZmjUrF043fC/swclSqMMSpVJxuetDoeriJO1BqTFR8QLbBnQuMCe+PTknRPPL/5YI/U3/vNB3uwauZwt897surysz9xX8E3xGzCrNEpWLHF/SrPs0anCE+mIWaTVB2a9npSzs0vRta6ApTYXMbQxEYga4pnJxuetDoeFV9yiIIRpy77gKoF9k6USQ6wFcTtlVx1WRRnd2hY963+9OZ13xYLVzneduisbqICNCYy2w7JTZV29m3H0TKs3XsaO46WBWyl5dz8YsxdndcsUQGAElst5q7OQ26+3DRy50mr5X7kPGnJtkPtB1cRJ3KPV1Z8QNUCe5omdxtIFOewy7UjihMlYcDlJExv/MjKbUel+rNy21HcPED/lhJgnCsQdoeGhWv268YsWrNfeEVN1ZU5al88+ZLD8VnU0fDKig+oqqyaLLkgoiguyiL3NoviVCVhZwQJjydxRroCsfNYmVSl4J3H9JdQUHVljtoXriJO5B6TFR9QVVm1vEquPooo7vuzcreTRHGqkrDuVrlVoEVxRrtsvuOo3KrUojietDomLh9B5B6TFR9wVlYFWq8/7Ell1SSr5GrJgjjZk7UobljvLhDddTBLrOkzW6faridxxrsCoWYZaJ60OiYuH0HkHpMVH3FWVm2ZSCRZI6RH9N8oeVASxSUJirTJxn1z4rzUwNhvTpzXjRl9dTfhekbhoWaMvrqbbozRrkBk9HG/NIAncTxpdUyqvuQQBSMmKz40MT0Z2x4bh/czR+KV6YPxfuZIbHtsnPSgz5mjUt2esJxMENcj0Vs3yJM4VclBiNmEV6cP1o15Zfpg4UHZaFcgRvbtis5RYboxnaPCMFIwOJInrY5LxZccomDE2UA+5k1lVUuoGbPHpurWNZk9NlV4lSIlPkbq74niVCYHzlWpF3+Sj9Kq+qbHE2MsyJ6aLnVQdl6BKKmobfPGigmNB3l/XYEIMZvw/LSBmLs6z23M89MGSiUZXPOo4+LyEUStMVkxuCG9ugBwn6w0Pq/PeVLXG98hc1tBdXLg7UHZiKsuO5OwrHUHUGK7vI5SUmw4sqZc51GSwZNWx8XlI4iaM2ma1q4rDNlsNlitVlRUVCA2NjbQ3VHK7tAw+veb3SYZzuRg22PjhCcwd5Uxne3IXmJ2tgO0nRwE4lK1UeqsuGKZfCIifZ6cv5msGNiOo2W4d9VOYdz7mSOlvoWpOqkzOSAiIm95cv7mbSADUz3bRdVtBSPenuBlcyKi4MVkxce8+cbvi9kuqk7qTA6IiMhfmKz4kLe3S4w224WIiCgQWGfFR1SsWeOc7eJuUJEG1tsgIqLgx2TFB4y2Zg0REVF7Zohkpa6uDoMHD4bJZMLevXsD3R2vqVqzxpn0uGMCkx4iIgp+hkhW/t//+3/o3r17oLuhjKpZPMZbqE89u0PDjqNlWLv3NHYcLWPiRURErQR8gO2nn36Kzz//HB999BE+/fTTQHdHCVWzeHyxUF9VbQN+88EeFJ2vQa8ukfjjPUMQHeH5bqCindz8YiWVXgHWWSEiCmYBTVZ++OEHZGZm4pNPPkFUVJTU/6mrq0Nd3eWTm81m81X3rpiqWTyqpy5PWbYV+05d3l6HSiqRnvUZBvWIxbr5Y6TaUNVObn5xm2volNjqMHd1HpZ7UAlXZdJDRETGE7DbQJqm4cEHH8TcuXNxww03SP+/nJwcWK3Wpp+ePXv6sJdXRtUsnmG9u0itujyst3h9oJYJhqt9p2yYsmyrsA1V7dgdGhau2a8bs3DNfqlbQs6kxzVRAS4nPTKzroiIyNiUJytZWVkwmUy6P19//TX+9Kc/wWazYdGiRR61v2jRIlRUVDT9nDx5UvVLUGJP0XmvngeAfx4tc5vwOGn/itNTVdvgNsFw2nfKhqraBr+0s/NoGS5UX9KNuVB9CTsFr0tl0kNERMalPFmZP38+Dh48qPuTnp6OzZs3Y+fOnQgPD0doaCj69esHALjhhhswc+ZMt+2Hh4cjNja22Y/R1Dc4sGqr+5WSAWDV1kLUNzh0Y97ffULq74niHnxju1Q7oriHVn8t1Y4obu2+Iql2RHGqkh4iIjI25WNW4uPjER8fL4x79dVX8eyzzzb9fubMGdx222344IMPcOONN6rull+9u+M4RF/mHVpj3KwxfdzGbPv+nNTfE8V9fbJSqh1R3DbJk74o7sPdcrdmPtxdjP+8y/3zXx09K9XOV0fP4qb+4n2SiIiMKWADbHv16tXs9+joaABA37590aNHj0B0SZkT5dVK4i7W26XakY3zll3ybopsnLfOXJCbBSUbR0RExmSIOivBpnec3MwmUVyo5LsjGxdsruoSqTSOiIiMyTCnuZSUFGiahsGDBwe6K167PyMFohIfZlNjnJ6MPnILFIriEqPk6o2I4gZ27yTVjijOIln+RBQ3qq/crR3ZOCIiMibDJCvBxBJqRuaYVN2YzDGpsAguiSz72XCpvyeKW3rXMKl2RHGrf3GTVDuiuFfuHizVjihuZJ+u6BwVphvTJSoMI/t0lfp7RERkTExWfGTR7WmYMza1VZ0UE4A5Y1Ox6PY0YRvREaEY1EN/ttOgHrHCyrG3DkhAiOCdDjE3xumxRoWhd1f9Wyq9u0bCKkggJgyWW1pBFBdiNuH5aQN1Y3KmDWQlWyKido7Jig8N6dUFibHNq8smxkZgSC9xETendfPHuE1YZCvGhphN+K/7hurG/Nd9Q6VO6l/+bpzbhKV310h8+btxUv1ZPkO/P8tnyPVnYnoyls8YiqQW2znZGuFRFVwiIjIuk6Zp7bpils1mg9VqRUVFhaFqruTmF2Pe6rxWRd2cp9/XPSwnv3htPkor65seS4ix4Omp6R6djHPzi/HUJ/k4W+VdOwBQUX0JP39rF85U1KK7NQJ/fnCE8IpKW/15/H/2orz2cr2ZrpFmLL1rMNcGIiIKcp6cv5ms+IDdoWH07ze7XTHZuTbQtsfGCU+o7tbQcfLk6kHOxgKs2NK6WJ3sbSlfYJJBRNQxeXL+Dviqy8FoV2G520QFaCyRX1xRi12F5cjo637wp92hYcGH3+r+rd9++C3GpyUJT/DuEhUATY97krDUNzjw7o7jOFFejd5xUbg/I0U4YLgtIWaT7jYgIiJisuIDpZVyRchEcduPnEO1oODbxXo7th85hzFXd3MbU9/gcJuoOK3YUojfTrhWKuHI2ViAVVsLm1XpXbrxIDLHBO4KDRERBS8OsPWBhJgIcZBE3Idfyy3SKIr789ZjUu3IxDmv0LRcTsChNSY8ORsLpP4WERGRLCYrPjAiNQ7J1ohW05adTGicrTIiVb+YW94J8crMMnEffn1cqh1RnKoFGomIiDzBZMUHQswmLJnceDukrTorALBkcppwnEn1Jbk1f0RxJ8rrpNoRxXmyQCMREZEqTFZ8ZGJ6Ml6fMRRJ1ua3epKsEdLTluOjLVJ/SzbOW8fLLiqNIyIiksEBtm6omO0yMT0Z49OSrnhq7l1DeuD5zw5JxemJj7bgB5caLXpxRERERsNkpQ0qZ7t4MzX352P6SCUrPx/TR/f57MnpmPue+1otrnF6Bvfsgnd3FgnbGdxTvkIvERGRCG8DtWCk2S6WUDPmjNVfEHHOWPGCiDGRcpVlRXHdO+uvC+RpHBERkQwmKy7qGxxYKahHsnKLf2e7OBdEbHnnyGySrzx77qLcAFtRnHOWkx6ZWU5ERESeYLLi4q1tha3W8mlJ+1ecrJp6O576ZD/uf+OfeOqT/agRFHlry6Lb07BvyW0YPyAB1yTFYPyABOxbcpv0LSlVdV+cs5z0pmTLzHIiIiLyBNcGcvHvr3+Fr09cEMbd0Lsz/mfeTcK4zHd2Y1NBaavHx6clYNUDw6X75e2aPs61ikoqattMxjxZqwhoXK8oe31BsyUFkq0RWDI5jascExGRFE/O37yy0ozsFQFxnLtEBQA2FZQi853dUn9JtKaPzBgaVXVfnCamJ2Pzb2/B/SN7YUz/eNw/shc2//YWJipEROQTTFZchNprlMTV1NvdJipOmwpKhbeEZNf0kRlDo6Lui1POxgJctyQX7+4swtbvz+HdnUW4bkkuS+0TEZFPcOqyi52n5BYgFMUtXrtPqp3Fa/fhhbuHuH3+za/k1vR586tjmHNzP2Gct3VfAPdXepyzpQDPVm+2OzSv+kNEvhesn9NgfV1A8L02Jis+8L/7SqTjXrjb/fOiqzOucTLJCuBd3RfZtYFkV2/m2BcKNqpOEEY60eTmFyNrXQFKbJc/p0mxEciaErjPqYrtk5tfjCVrD+CHysuzIBNjwpE99TqPX5eKIqIqBeOxlcmKDzSIFtCRjpMd++yfMdKerA00S1CoLje/GPNW57XqeUlFLeatzvP41hRRoKk6QTQmBwdQYrt8Ek2KDUfWFM9Pot7KzS/G3NWtC0qW2Goxd3Uelgfgc6piO7t7XT9U1nn8ulQWEVUhWI+tHLPiA727yE0VFsVNSEuSakc2zluF5+TW/BHF2R0astcXtJliOR/LXl8Au2TSRxRozhOE6wkUuHyCyM0vlm5n7uq8ZokKAJTYGk+isu2oYHdoWLhmv27MojX7/fo5VbGd7Q4NCz78Vjfmtx9+K/W6jFREFAjuYyuTFRdzx/RWEvfh3NFS7YjiHrwpVTjvyPSvOH/4wSY3pkcUt6uwvNXBxpUGoLiiFrsKyz3pHlFAqDpByCQHC/2YHOw8VoYL1Zd0Y85XX8LOY2V+6Y+q7bz9yDlUCyY3XKy3Y/uRc7oxRiwiGszHViYrLn436TolcXHRFnQTLArYLdqCOEGMJdSM2YJy+7Mlyu2r0jVGbqFDUVxppVzSIxtHFEiqThA7j4qTgwvVl7DzqH+Sgx2Sf0c2zluqtvNH35yS+nuiuLe+kiwi+pV8EVFvBfOxlcmKixCzCctnDNWNWT5jqNRArt1PjnebsHSLtmD3k+Ol+qSi3L4qYWa53UUUp6qiLpERqDpB7Dim/03e0zjvGWvMnKrtfOpCtVQ7orjPC+QmUsjGqRDMx1YOsG1hYnoyls8Yit/8JQ81Lp/BSBPwx595NjBp95PjUV5Vj+krt6O0sh4JMRb8dfYo4RWVlhbdnobfTrhWyWhzb0bRD5FcdXmIYNVl5xpDooq6XGOI2gN1Jwh1RSlVyOgTj2VfHJWK8wdV2/mqzlFSlcqv6hwl9feMJJiPrUxW3IiNtqCmsr7Z71ciLtqCzxfc4nV/LKFm4QwbEW9H0SdLrqYsinNW1J23Og8mNP9ediUVdYkCSdUJIqNvVyz74ojw711p6QFPjezbFZ2jwnRvTXWOCsNIP/VH1Xb+92E9sPbbM8K/9+/Deug+Pz4tSSrpGe+nCRBAcB9beRuoBedo/B9cEhUA+KGy3u+j8VVSMYpe5arLKivqEgWSquUsRvZpTA70dIkKw8g+/kkOQswmPD9toG7M89MG+u3Ep2o7j+oXjyhLiG5MJ0sIRvXTv2L0H5ITG2TjVAnWYysXMnRhd2gY9uwm4TeJb54c364yU+dChu4Gp3mykKEz6QHazto9/TAYqfgVkTd8Wf/DKVB1TYxS98XZH6NsZ7212wD/jyt01R6OrZ6cv5msuPjq+3P42Rv/FMb9ZdaNuKm/3H1aI1S03HG0DPeu2imMez9zpNQlZpUHr/bwgSKSpaqyasuKsYGuPmq0z6mvKtheyXEsZ2MBVm4pbPXlbXYAE5X2wpPzN8esuPBkNL5MspKbX4xFf8vDeZf6Tl3CgZy7Pft2lJtfjKc+3oezFxuaHuvWKRTP/GSQVDu+mc7m7kKsvGAsCU0dmzfLWTipWMNLNRWvSyUjbWeVEyDIPSYrzagbje/uMuP5OnhUztldO2cvNki3o3I6m7tSzj/YPCvlHKwloYlUMFpyEKxUbWcVEyBIH1M/FzdKTucSxdkdmu79UKAxYZGpaDn/vT26MfPf2yNsZ+BVVt3nZeNUVuoM1pLQRESkHpMVFyUVNUriNubJVUgUxW05WCpc7LDBoWHLQf3VmX/55y+l+iOKU1VBMphLQhMRkXoBT1Y2bNiAG2+8EZGRkYiPj8e0adMC1pfHBOtyyMYt+FiuHVHci/93WKodUdyWIrmxKKI4VWNfgrkkNBERqRfQMSsfffQRMjMz8dxzz2HcuHHQNA3798ud6H1B9q6DKK7BLteQKK6iRn+dEE/jvKVq7Eswl4QmIiL1ApasNDQ04OGHH8YLL7yAWbNmNT1+zTXXBKpLCDUBDRJ5RqhgfG1kmBnVl8QrbUaG6V/YuiYpBifPi29NXZMUI4xRQVUFyWAuCU1EROoF7DZQXl4eTp8+DbPZjCFDhiA5ORmTJk3CgQMHdP9fXV0dbDZbsx9Vch++WUmcqnZevmeIVDuiuF//qKdUO6I4VRUkVbVDREQdQ8CSlWPHjgEAsrKy8OSTT+J///d/0aVLF9x8880oL3c/sDInJwdWq7Xpp2dPuROxjH5J0cJJyaZ/xenpFR8F0RT7UHNjnJ7oiFAM6qFfKGdQj1hER+hfIOvbTW5qnkycqlLOwVoSmoiI1FNewTYrKwvZ2dm6Mbt378bhw4fxs5/9DCtWrMDs2bMBNF416dGjB5599lnMmTOnzf9bV1eHurrLVdZsNht69uyppIKtU+rCDW5vTxQ+f4d0O/0e34CGNu4GhZqBI8/JtzNl2VbsO9X6CtKgHrFYN3+M8P+rrmALGKMyLxERtV8BrWA7f/58TJ8+XTcmJSUFlZWVAIC0tMvliMPDw9GnTx8UFRW5/b/h4eEIDw9X01k3Cp+/A0dKqjDp1S9xyQGEmYFPf32z8IpKS0eeuwNF56ox8ZUvUXPJgcgwM3Ifvll4RaWldfPHoKq2Ab/5YA+KztegV5dI/PGeIcIrKk6+GCOiqpgSi18REZGI8mQlPj4e8fHiUvTDhg1DeHg4Dh06hNGjRwMALl26hOPHj6N3796qu+WxfknR+N6Dqx/u9IqPQsEzk7xuJzoiFKtmDr+i/xvMy4YTEVHwC9iYldjYWMydOxdLlizB559/jkOHDmHevHkAgLvvvjtQ3QpaHCNCRETtVUDrrLzwwgsIDQ3F/fffj5qaGtx4443YvHkzunTpEshuGVJ9g8PrhbKMuEAaERGRiPIBtv7myQCd9ipnYwFWbCls9ficK1iCnANaiYjU4nH1ygR0gG2wKK+qx/SV21FaWY+EGAv+OnsU4qItHrfj7U7sLlEB0PS4bMKSm1+M7PUFzdblSbZGYMnkNN4GIiK6Ajyu+gevrLRh+LObcLaqvtXj3aIt2P3keOl2cvOLkbWuACW2yztxUmwEsqbI7cT1DQ5c/eSnwrjDz04S3hLKzS/GvNV5rWYDOdMmjlshIvIMj6ve8eT8HfCFDI3GXaICAGer6jH82U1S7eTmF2Pu6rxmiQoAlNhqMXd1HnLzi4VtvLHtqNTfEsXZHRqy1xe0OW1Z+9dP9voC2GUXRyIi6uBEx1WAx1WVmKy4KK+qd5uoOJ2tqke5IMbu0LBQsDLzojX7hTvxmrzTus/Lxu0qLG92ibItxRW12FXovnIwERFdJjquauBxVSUmKy7+7fdyV01EcTuPleFCtf5KyOerL2HnsTLdmMraBqn+iOJaXt3xNo6IqKMrrZQ7XsrGkT4mKy7K9fML6bgdR/WTENm4vt06SbUjiiuvqtN93tM4IqKOLiEmQhzkQRzpY7LiE7L3KPXjZo/tK9WKKC6uk9wsJtk4IqKOzrmMibu5nSY0zgryZBkTco/Jig/cmCq31o0obnT/bggVTHMONZswun833Zgka6RUf2TjiIKN3aFhx9EyrN17GjuOlnFQJAk5lzEB0Cph4TIm6rHOiovldw3G3I/2SsXpMZvkdk6ZuOiIUN3xLzESixk6vwHoDQbz9BtAsK66bLTXpaJysUrB+LpU1snwZsFRan+cy5i03H+SWGdFOX6KXEwcfhUgkaxMHH6V7vPnLsqN/RDF7Soslxqou6uwXHflYuc3gLmr89zGePINoLF+zAGU2C73Pyk2HFlTrvPow2m0Ykq5+cVY/Ek+Sl1meyVEW/D0nekBeV05GwuwamshXL/kL914EJljAlO52Iivy1vu6mSUVNRi3uo8j+pkTFm2FftO2Zp+P1RSifSszzCoRyzWzR/jUb9UJXOqksuaejue21iA42XVSOkahcdvT0OkJcTjdlQx0pccLmPiH0xWWpgzNtVtxVjn8yKqBl6pnMWzp+i88HmZg7KzfkzrPtRh7uo8LJc8uKs8Sajg7nWVVtUH5HW5q1zs0AJTudiIr8tbojoZJjTWyRifliQ88bRMVFztO2XDlGVbpRMWVcmcquQy853d2FRQ2vT71u+Bd3cWYXxaAlY9cGUrwXsjN78YS9bm44fKy18qEmMsyJ7q2ZcKlULMJt0vjOQ9jllxUd/gwMqt7hMVAFi5tRD1DQ7dmGG9u0B0h8dkaozTc1YyWRHF1Tc4sFInAQOAlVvEr0umfsxCifoxviim5M2YA7tDw6//ulc35uG/7vXb66pvcGCVYD9cJbEfApeTjJa3AJ1JhkxxQlVFBVW+LhVU1cmoqm1wm6g47TtlQ5VEKQJnMtdyUzqTuZyNBcI2ADXvO9A6UXG1qaAUme/slmpHFeeXCtdEBQB+qKyXLrZJ7ROTFRdvbz8O0eIDmtYYp2f38XKpdnYf1z8I7j91Qb8Rybi3txcK5ydp/4rTs/OouH7MhepL2CmYkq26mFJufjFG/34z7l21Ew//dS/uXbUTo3+/WfrAte3wWeEJsq7BgW2Hz+rGqHpd7+443upk1ZJDa4zToyp5UlVUUNXrUkVVnYxHPtgj1Y4oTtWXJVXve0293W2i4rSpoBQ19XbdGFXsDg0LPvxWN2bBh99ycHSQYrLi4p+FcvVRRHGq6qzskUxWRHG7j+vfApKN237snFQ7ojiVxZRUfINcufWYVH9Ecape1/Gyaql2RHGqkidVtyNPlMu9Ltk4b8VHhyuJO1RSKdWOKE7VlyVV7/tzkldxZOO8tf37c6gWJEbV9XZs/17uOEXtC5MVFyWCb4/ycWrqrIimLcvGdZIcCCeKO3O+RqodUZyqMT2qvkGeqZB8XYI4Va9Ldm1RUZyq5ElVUcGeXaKk2pGN85qajymskWFSzYjiRFdaZeOMljSr8tGeU0rjqH1hsuLCEiKXHIjiMvrES7UjihvVV64dUdy0oT2k2hHFde8sV4dFFKeqmJKqb5DdJevLiOJUvS7Zqa6iOFXJk6qigtcmxki1IxvnLVWz9n77o6ul2hHFRUl+qRDFqbpilNJVLmmUjfOW6KqKp3HUvjBZcdEzTvKbnyBuZN+u6Byl/y2qc1QYRgpGjz/14+uk+iOKG9UvXnjVpFN4CEb180/ypKqYkqpvkLPH9JFqRxSn6nWpuqKmKnlSVVSwvEZ/AVBP47ylKpkbOyBBqnjj2AEJujF3DZH7UiGMU3TF6HHJmUeycd4aniJXB0o2jtoXJisu/n1oTyVxIWYTnp82UDfm+WkDhSetSEsIxqfpH+DGpyUI6x2EmE148afX68a8ePf1wv6oSsKAy8WUkqzNTwRJ1gjpabCqTjajr+4mrGERHmrG6Kv1KwUDal6XqitzqpInZ9KjRybpMdpaKqqSuRCzCcvuG6Ibs+y+IcLtPKp/vPCqSZQlBKP667/vqq4YqTr+qDJzVIrULMuZo1L80h/yLyYrLkb1j5f6hiQ6WKi06oHhbg8YntQ5mJiejOUzhiIptvmJINkaIV1DRFUS5tqnbY+Nw/uZI/HK9MF4P3Mktj02TrpWgsqTzavTB+vGvDJ9sN9el9GSQmfSo7edPUl6jLKWispy6c7PV7dOzW/NdesU6tHn6yXBl4qXfir+UqEyKVR1/FHBEmrG7DH6da5mj0kNaIVn8h2TJjuaz6BsNhusVisqKioQGxvrVVv1DQ5c8+SnuldHTQAOPTtJ9wNhd2gY/fvNbsdTmNB4stj22DjpE6CqCpKqKpmqqGCrgnM2END8qrbzFXlSXK6tCraBKjblrkidk+wJ0MkoFWxVvl+qqKykrGo7e1P0zHn8KamobfNYFsjjjwptFc0zmxCQCsjkHU/O30xWXLyx9Rie2XBQGPfUHQMwS2f8wo6jZbh31U5hO+9njmy3VQ+NVO7aaCcbVYyUFDoZJelRzUjvu4r+GDEpVMlIa0vRlWOycoUWr83HOztOCOMeyOiNp6emu31+7d7TeFhQERVovLUwdbD+OkMkx2gnG1X4uuhKGTEpJHLlyfmbawO56C05G0gUZ7SBhB1BsK7NwddFV4oL7FEw4XUzF/dnpED0OTabGuP0GG0gIRF1TM6kcOrgq5DRtysTFWq3mKy4sISakSkYbZ4pMdpc5SwDIiKijo7JSguLbk/DnLGpra6wmE3AnLHyo81VTBklIiIiDrB1S9Vocw4kJCIiao0DbBWwhJp1pyfL4kBCIiIi7/A2EBERERkakxUiIiIyNCYrREREZGhMVoiIiMjQmKwQERGRoTFZISIiIkMLaLJy+PBhTJ06FfHx8YiNjcVNN92EL774IpBdIiIiIoMJaLJyxx13oKGhAZs3b8Y333yDwYMH48c//jFKSkoC2S0iIiIykIAlK+fOncORI0ewcOFCDBo0CP3798fzzz+P6upqHDhwIFDdIiIiIoMJWLLStWtXDBgwAO+88w4uXryIhoYGrFixAomJiRg2bJjb/1dXVwebzdbsh4iIiIJXwMrtm0wmbNq0CVOnTkVMTAzMZjMSExORm5uLzp07u/1/OTk5yM7O9l9HiYiIKKCUX1nJysqCyWTS/fn666+haRp++ctfIiEhAVu3bsWuXbswdepU/PjHP0ZxcbHb9hctWoSKioqmn5MnT6p+CURERGQgylddPnfuHM6dO6cbk5KSgq+++goTJkzA+fPnm6222L9/f8yaNQsLFy6U+nu+WnWZiIiIfCegqy7Hx8cjPj5eGFddXQ0AMJubX9wxm81wOByqu0VERETtVMAG2GZkZKBLly6YOXMmvv32Wxw+fBi/+93vUFhYiDvuuCNQ3SIiIiKDCViyEh8fj9zcXFRVVWHcuHG44YYbsG3bNqxduxbXX399oLpFREREBqN8zIq/ccwKERFR++PJ+ZtrAxEREZGhBazOCrVvdoeGXYXlKK2sRUJMBEakxiHEbAp0t4iIKAgxWWknVCUH9Q0OvLvjOE6UV6N3XBTuz0iBJdSzC2y5+cXIXl+A4orapseSrRFYMjkNE9OTPe4TERGRHo5ZaQdy84uRta4AJbbLyUFSbASypniWHORsLMCqrYVwuLzjZhOQOSYVi25Pk+7LvNV5aLnTONOm12cMZcJCRERCHLMSRHLzizF3dV6zRAUASmy1mLs6D7n57qv9usrZWIAVW5onKgDg0IAVWwqRs7FA2IbdoSF7fUGrRAVA02PZ6wtgb/lHiIiIvMBkxcDsDg0L1+zXjVm0Zr8wOahvcGDllkLdmJVbClHfoF+Mb1dhebNbPy1pAIorarGrsFy3HSIiIk8wWTGwncfKcKH6km7M+epL2HmsTDfmra8K27wa4kr7V5ye0kr3icqVxBEREclgsmJgO47qJyGycZ8XlEi1I4pLiImQakc2joiISAaTFQOTHfssipMdQSKKG5Eah2RrBNzNQTKhcVbQiNQ4yb9IREQkxmTFwDpHhSmJuyYxWqodUVyI2YQlkxtnDbVMWJy/L5mcxnorRESkFJMVA4uXvJ0iihvWS+5Kh0zcxPRkvD5jKJKszf9mkjWC05aJiMgnWBTOwJJi5ZIVUVz3LlFS7cjGTUxPxvi0JFawJSIiv2CyYmDOMSJ604VlxoioasdViNmEjL5dpeOJiIiuFG8DGZhzjIgJbY8RMUFujIhrO22RbYeIiCgQmKwYnKoxIs52klu0k8yxJoZmd2jYcbQMa/eexo6jZawOTEQdEtcGaieMtJAh+QcXjCSiYObJ+ZvJSgfCk1/7wQUjiSjYcSFDasV58ms5yLakohbzPFgQkXyPC0YSETXHZKUD4MmvfeGCkUREzTFZ6QB48mtfuGAkEVFzTFY6AJ782hcuGElE1ByLwrlRVduA33ywB0Xna9CrSyT+eM8QREe0z83Fk1/74iziV1JR2+atOxMap65zwUgi6ija59nXx6Ys24p9p2xNvx8qqUR61mcY1CMW6+aP8aits7Y6/OS1bSi/eAlxncLw8S9Ho1tsuOou6/JFBVtVVE3JVtWOETiL+M1bnQcTmq+GzQUjiagjYrLSQstExdW+UzZMWbZVOmEZlPUZbLUNTb9XX7Bj+HN/R2xEKPZl3eZRv2rq7XhuYwGOl1UjpWsUHr89DZGWEKn/G2I2If2qWN1kJf2qWI9Oft70xyk3vxiLP8lHaVV902MJ0RY8fWe6R9Nyc/OLsWTtAfxQWdf0WGJMOLKnXufx9F6jJE/OIn4tp5oncao5EXVArLPioqq2AelZnwnj8rNuE94SapmotORJwpL5zm5sKiht9fj4tASsemC48P/XNzhwzVOfQu+dNpmAQ89MkioQ521/gMYEY+7qPLfPL5esI6KqHWdbKurQqKxnE0xXjIiIXLHOyhWa++4uJXFnbXW6iQoA2GobcNZWpxsDuE8MAGBTQSky39ktbOPt7cd1ExUA0LTGOH/0x+7Q8Ou/7tWNefive4VTqe0ODQ8raAdQV4dGdT0b54KRUwdfhYy+XZmoEFGHxGTFxbaj55XETV22VaodUVxNvd1tYuC0qaAUNfV23Zjdx8uk+iOKU9WfbYfPor7BoRtT1+DAtsNnhe3UKWhHVR0a1rMhIvINJis+cPZivThIIu65jQVS7YjioixyQ5NEcUs3yPVHFLdi61GpdkRxq7Yek2pHFKeqDg3r2RAR+QaTFR8IC5G7VC+KO15WLdWOKO6uoT2k2hHFfXvqglQ7orhjZy9KtSOKq6i9JNWOKE5VHRrWsyEi8g0mKz4wZ0xfJXEpXaOk2hHFjeoXjyjBTJ1OlhCM6hevGxMbESbVH1FcrGS9GlHc9T2sUu2I4lTVoWE9GyIi32Cy4uLq+EglcfNu7SfVjiju8dvTpNoRxYWYTXjpp9frxrz40+uFgzdnj+kj1R9R3E8kr/SI4p644zqpdkRxzjo07l69CXJ1aFS1Q0REzTFZcfG3X8rVTxHFWULNmDM2VTdmzthU4TThSEsIxqcl6MaMT0uQqm8yMT0Zy2cMRWJM84J0SbHh0tN7R1/dTdjn8FAzRl/dTTdm1mi5pEcUp2r7OIuwAWiVaHhShE1VO0RE1ByTFRfWqDD07qp/1aR310hYo8S3QxbdnoY5Y1PbPGnNGZuKRZJXTVY9MNztCdmTuiZAY8KyfdGP8H7mSLwyfTDezxyJrxb+SLr2R4jZhFenD9aNeWX6YOHJWFUyB6jbPs4ibEnW5rdokqwReN2DWi2q2iEiostYFK4NN7+wGSfKalo93rtrJL783TiP2qpvcODdHcdxorwaveOicH9GitRJuCUVFWNVaavybGKMBdlTPas8m7OxACu2FLZ63JNkzknV9jFKBVsiomDnyfmbyYobFdWX8PO3duFMRS26WyPw5wdHSF1R6ShUnYxVJXNERNS+GCZZWbp0KTZs2IC9e/fCYrHgwoULrWKKiorw0EMPYfPmzYiMjMR9992HP/zhD7BYLFJ/w1fJChEREfmOJ+dvny5kWF9fj7vvvhsZGRl44403Wj1vt9txxx13oFu3bti2bRvKysowc+ZMaJqGP/3pT77sGhEREbUTPk1WsrOzAQBvvfVWm89//vnnKCgowMmTJ9G9e3cAwIsvvogHH3wQS5cu5ZUSIiIiCuxsoB07diA9Pb0pUQGA2267DXV1dfjmm2/a/D91dXWw2WzNfoiIiCh4BTRZKSkpQWJiYrPHunTpAovFgpKSkjb/T05ODqxWa9NPz549/dFVIiIiChCPk5WsrCyYTCbdn6+//lq6PZOp9QwSTdPafBwAFi1ahIqKiqafkydPevoSiIiIqB3xeMzK/PnzMX36dN2YlJQUqbaSkpLwz3/+s9lj58+fx6VLl1pdcXEKDw9HeHh4m88RERFR8PE4WYmPj0d8vP6Cd7IyMjKwdOlSFBcXIzm5sZjY559/jvDwcAwbNkzJ3yAiIqL2zaezgYqKilBeXo6ioiLY7Xbs3bsXANCvXz9ER0djwoQJSEtLw/33348XXngB5eXlePTRR5GZmcmZQERERATAx8nK4sWL8fbbbzf9PmTIEADAF198gVtuuQUhISHYsGEDfvnLX+Kmm25qVhSOiIiICGC5fSIiIgoAT87fXISFiIiIDI3JChERERkakxUiIiIyNCYrREREZGhMVoiIiMjQmKwQERGRoTFZISIiIkNjskJERESGxmSFiIiIDI3JChERERkakxUiIiIyNCYrREREZGg+XXWZ1LE7NOwqLEdpZS0SYiIwIjUOIWZToLtFRETkc0xW2oHc/GJkry9AcUVt02PJ1ggsmZyGienJAewZERGR7/E2kMHl5hdj3uq8ZokKAJRU1GLe6jzk5hcHqGdERET+wWTFwOwODdnrC6C18Zzzsez1BbA72oogIiIKDkxWDGxXYXmrKyquNADFFbXYVVjuv04RERH5GZMVAyutdJ+oXEkcERFRe8RkxcASYiKUxhEREbVHTFYMbERqHJKtEXA3QdmExllBI1Lj/NktIiIiv2KyYmAhZhOWTE4DgFYJi/P3JZPTWG+FiIiCGpMVg5uYnozXZwxFkrX5rZ4kawRenzGUdVaIiCjosShcOzAxPRnj05JYwZaIiDokJivtRIjZhIy+XQPdDSIiIr/jbSAiIiIyNCYrREREZGhMVoiIiMjQmKwQERGRoTFZISIiIkNjskJERESGxmSFiIiIDI3JChERERkakxUiIiIyNCYrREREZGg+TVaWLl2KUaNGISoqCp07d271/Lfffot7770XPXv2RGRkJAYMGIBXXnnFl10iIiKidsanawPV19fj7rvvRkZGBt54441Wz3/zzTfo1q0bVq9ejZ49e2L79u2YPXs2QkJCMH/+fF92jYiIiNoJk6Zpmq//yFtvvYVHHnkEFy5cEMY+9NBDOHjwIDZv3izVts1mg9VqRUVFBWJjY73sKREREfmDJ+dvw626XFFRgbi4OLfP19XVoa6urul3m83mj24RERFRgBhqgO2OHTvw4YcfYs6cOW5jcnJyYLVam3569uzpxx4SERGRv3mcrGRlZcFkMun+fP311x535MCBA5g6dSoWL16M8ePHu41btGgRKioqmn5Onjzp8d8iIvIFu0PDjqNlWLv3NHYcLYPd4fO77O2qP0RXyuPbQPPnz8f06dN1Y1JSUjxqs6CgAOPGjUNmZiaefPJJ3djw8HCEh4d71D4Rka/l5hcje30Biitqmx5LtkZgyeQ0TExP7vD9IfKGx8lKfHw84uPjlXXgwIEDGDduHGbOnImlS5cqa5d8y+7QsKuwHKWVtUiIicCI1DiEmE2B7pZhcPv4h1G2c25+MeatzkPL6xYlFbWYtzoPr88Y6tcEQXV/jLKdVQvW1xWMfDrAtqioCOXl5SgqKoLdbsfevXsBAP369UN0dDQOHDiAW2+9FRMmTMCCBQtQUlICAAgJCUG3bt182TXyAr+x6eP28Q+jbGe7Q0P2+oJWiQEAaABMALLXF2B8WpJfToSq+2OU7axasL6uYOXTAbaLFy/GkCFDsGTJElRVVWHIkCEYMmRI05iWv/3tbzh79iz+8pe/IDk5ueln+PDhvuwWecH5jc31Aw5c/saWm18coJ4ZA7ePfxhpO+8qLG/VD1cagOKKWuwqLG93/THSdlYpWF9XMPNpsvLWW29B07RWP7fccguAxsG6bT1//PhxX3aLrpDoGxvQ+I2tow7i4/bxD6Nt59JK94nBlcR5S1V/jLadVQnW1xXsDDV1mYzNaN8gjYbbxz+Mtp0TYiKUxnlLVX+Mtp1VCdbXFeyYrJA0o32DNBpuH/8w2nYekRqHZGsE3I3+MKFxLMSIVPfFLo3YH6NtZ1WC9XUFOyYrJM1o3yCNhtvHP4y2nUPMJiyZnAYArRIE5+9LJqf5bZaJqv4YbTurEqyvK9gxWSFpRvsGaTTcPv5hxO08MT0Zr88YiiRr8xNckjXC79OWVfXHiNtZhWB9XcHOLwsZ+hIXMvQv5yh6AM0GqDk/+IE4MBsJt49/GHU7G61uh7f9Mep29lawvq72xpPzN5MV8hjrE+jj9vEPbmf/CNbtHKyvqz1hskI+Z7RvkEbD7eMf3M7+EazbOVhfV3vBZIWIiIgMzZPzNwfYEhERkaExWSEiIiJDY7JCREREhsZkhYiIiAyNyQoREREZGpMVIiIiMjQmK0RERGRoTFaIiIjI0JisEBERkaExWSEiIiJDY7JCREREhsZkhYiIiAwtNNAdIDlcHZSIiDoqJivtQG5+MbLXF6C4orbpsWRrBJZMTsPE9OQA9oyIiMj3eBvI4HLzizFvdV6zRAUASipqMW91HnLziwPUMyIiIv9gsmJgdoeG7PUF0Np4zvlY9voC2B1tRRAREQUHJisGtquwvNUVFVcagOKKWuwqLPdfp4iIiPyMyYqBlVa6T1SuJI6IiKg9YrJiYAkxEUrjiIiI2iMmKwY2IjUOydYIuJugbELjrKARqXH+7BYREZFfMVkxsBCzCUsmpwFAq4TF+fuSyWmst0JEREGNyYrBTUxPxuszhiLJ2vxWT5I1Aq/PGMo6K0REFPRYFK4dmJiejPFpSaxgS0REHRKTlXYixGxCRt+uge4GERGR3/E2EBERERkakxUiIiIyNJ8mK0uXLsWoUaMQFRWFzp0768aWlZWhR48eMJlMuHDhgi+7RURERO2IT5OV+vp63H333Zg3b54wdtasWRg0aJAvu0NERETtkE+TlezsbPzmN7/BwIEDdeNef/11XLhwAY8++qgvu0NERETtUMBnAxUUFODpp5/GP//5Txw7dizQ3SEiIiKDCWiyUldXh3vvvRcvvPACevXqJZWs1NXVoa6urul3m83myy4SERFRgHl8GygrKwsmk0n35+uvv5Zqa9GiRRgwYABmzJgh/fdzcnJgtVqbfnr27OnpSyAiIqJ2xKRpmubJfzh37hzOnTunG5OSkoKIiMvl4d966y088sgjrWb5DB48GPv374fJ1FiJVdM0OBwOhISE4IknnkB2dnarttu6stKzZ09UVFQgNjbWk5dCREREAWKz2WC1WqXO3x7fBoqPj0d8fPwVd87VRx99hJqamqbfd+/ejZ///OfYunUr+vbt2+b/CQ8PR3h4eNPvzlyLt4OIiIjaD+d5W+aaiU/HrBQVFaG8vBxFRUWw2+3Yu3cvAKBfv36Ijo5ulZA4r9gMGDBAWJfFqbKyEgB4O4iIiKgdqqyshNVq1Y3xabKyePFivP32202/DxkyBADwxRdf4JZbblHyN7p3746TJ08iJiam6XaSKs5bTCdPnuQtJh/idvYPbmf/4Hb2D25n//HVttY0DZWVlejevbsw1uMxKx2JJ/fT6MpxO/sHt7N/cDv7B7ez/xhhW3NtICIiIjI0JitERERkaExWdISHh2PJkiXNZh+RetzO/sHt7B/czv7B7ew/RtjWHLNCREREhsYrK0RERGRoTFaIiIjI0JisEBERkaExWSEiIiJDY7LixmuvvYbU1FRERERg2LBh2Lp1a6C7FFTaWr07KSkp0N0KClu2bMHkyZPRvXt3mEwmfPLJJ82e1zQNWVlZ6N69OyIjI3HLLbfgwIEDgelsOybazg8++GCrfXzkyJGB6Ww7lZOTg+HDhyMmJgYJCQm48847cejQoWYx3J/VkNnWgdynmay04YMPPsAjjzyCJ554Anv27MGYMWMwadIkFBUVBbprQeW6665DcXFx08/+/fsD3aWgcPHiRVx//fVYtmxZm8//53/+J1566SUsW7YMu3fvRlJSEsaPH9+0zhbJEW1nAJg4cWKzfXzjxo1+7GH79+WXX+Khhx7Czp07sWnTJjQ0NGDChAm4ePFiUwz3ZzVktjUQwH1ao1ZGjBihzZ07t9lj1157rbZw4cIA9Sj4LFmyRLv++usD3Y2gB0D7+OOPm353OBxaUlKS9vzzzzc9Vltbq1mtVm358uUB6GFwaLmdNU3TZs6cqU2dOjUg/QlWpaWlGgDtyy+/1DSN+7MvtdzWmhbYfZpXVlqor6/HN998gwkTJjR7fMKECdi+fXuAehWcvv/+e3Tv3h2pqamYPn06jh07FuguBb3CwkKUlJQ027/Dw8Nx8803c//2gX/84x9ISEjA1VdfjczMTJSWlga6S+1aRUUFACAuLg4A92dfarmtnQK1TzNZaeHcuXOw2+1ITExs9nhiYiJKSkoC1Kvgc+ONN+Kdd97BZ599hlWrVqGkpASjRo1CWVlZoLsW1Jz7MPdv35s0aRL+8pe/YPPmzXjxxRexe/dujBs3DnV1dYHuWrukaRoWLFiA0aNHIz09HQD3Z19pa1sDgd2nQ33+F9opk8nU7HdN01o9Rldu0qRJTf8eOHAgMjIy0LdvX7z99ttYsGBBAHvWMXD/9r177rmn6d/p6em44YYb0Lt3b2zYsAHTpk0LYM/ap/nz52Pfvn3Ytm1bq+e4P6vlblsHcp/mlZUW4uPjERIS0iorLy0tbZW9kzqdOnXCwIED8f333we6K0HNOeOK+7f/JScno3fv3tzHr8CvfvUrrFu3Dl988QV69OjR9Dj3Z/Xcbeu2+HOfZrLSgsViwbBhw7Bp06Zmj2/atAmjRo0KUK+CX11dHQ4ePIjk5ORAdyWopaamIikpqdn+XV9fjy+//JL7t4+VlZXh5MmT3Mc9oGka5s+fjzVr1mDz5s1ITU1t9jz3Z3VE27ot/tyneRuoDQsWLMD999+PG264ARkZGVi5ciWKioowd+7cQHctaDz66KOYPHkyevXqhdLSUjz77LOw2WyYOXNmoLvW7lVVVeHIkSNNvxcWFmLv3r2Ii4tDr1698Mgjj+C5555D//790b9/fzz33HOIiorCfffdF8Betz962zkuLg5ZWVm46667kJycjOPHj+Pxxx9HfHw8fvKTnwSw1+3LQw89hPfeew9r165FTExM0xUUq9WKyMhImEwm7s+KiLZ1VVVVYPfpgMxBagf+67/+S+vdu7dmsVi0oUOHNpu+Rd675557tOTkZC0sLEzr3r27Nm3aNO3AgQOB7lZQ+OKLLzQArX5mzpypaVrjdM8lS5ZoSUlJWnh4uDZ27Fht//79ge10O6S3naurq7UJEyZo3bp108LCwrRevXppM2fO1IqKigLd7Xalre0LQHvzzTebYrg/qyHa1oHep03/6iQRERGRIXHMChERERkakxUiIiIyNCYrREREZGhMVoiIiMjQmKwQERGRoTFZISIiIkNjskJERESGxmSFiIiIDI3JChERERkakxUiIiIyNCYrREREZGhMVoiIiMjQ/j8fDtwo2kMO0wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for GA\n",
      "In year/election 2020 Dem and GOP total votes were 2474466 2461834 for a stateGOP of 0.49872\n",
      "There are a total of 2698  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1877962 2089101 for a stateGOP of 0.52661\n",
      "There are a total of 2698  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for GA\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv GA2698patchedWithCutsVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 2698 drawn districts.  This state has 14 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c38b293-7a46-4106-a972-b4868206bc2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"d\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 2474466 2461834 -12631\n",
      "ensemble : 2475299 2460898 -14401\n",
      "the true and ensemble state GOP leans are 0.49872 0.49854128212873894\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7213 7211 26\n"
     ]
    }
   ],
   "source": [
    "print(nDrawnDistricts,nHDs, nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
